
#ignoring warnings
import warnings
warnings.filterwarnings("ignore")


# Libraries to help with reading and manipulating data
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# libaries to help with data visualization
import matplotlib.pyplot as plt       # matplotlib.pyplot plots data
%matplotlib inline
import seaborn as sns
pd.set_option('mode.chained_assignment', None)

#restrict float value to 3 decimal places
pd.set_option('display.float_format',lambda x: '%.3f' % x)
%matplotlib inline

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

from sklearn.linear_model import LogisticRegression

# To get diferent metric scores


# Library to split data
from sklearn.model_selection import train_test_split

from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    ConfusionMatrixDisplay,
    #plot_confusion_matrix,
    precision_recall_curve,
    roc_curve,
)


#allow access to google drive
from google.colab import drive
drive.mount('/content/gdrive')

Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).


#paths
datapath= "/content/gdrive/MyDrive/Projects/ML/Data/"

#load csv into panda
ploan_df = pd.read_csv(datapath+ 'Loan_Modelling.csv')


# copying data to another variable to avoid any changes to original data
ploancp_df = ploan_df.copy()


#Function to show %
def autopct_format(values):
        def my_format(pct):
            total = sum(values)
            val = int(round(pct*total/100.0))
            return '{:.1f}%\n({v:d})'.format(pct, v=val)
        return my_format


# function to create labeled barplots

def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


#function to show stacked barplots
def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()

### function to plot distributions wrt target

def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()


# defining a function to compute different metrics to check performance of a classification model built using sklearn

def model_performance_classification_sklearn_with_threshold(model, predictors, target, threshold=0.5):
    """
    Function to compute different metrics, based on the threshold specified, to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # predicting using the independent variables
    pred_prob = model.predict_proba(predictors)[:, 1]
    pred_thres = pred_prob > threshold
    pred = np.round(pred_thres)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {
            "Accuracy": acc,
            "Recall": recall,
            "Precision": precision,
            "F1": f1,
        },
        index=[0],
    )

    return df_perf


# defining a function to plot the confusion_matrix of a classification model built using sklearn
def confusion_matrix_sklearn_with_threshold(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix, based on the threshold specified, with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    pred_prob = model.predict_proba(predictors)[:, 1]
    pred_thres = pred_prob > threshold
    y_pred = np.round(pred_thres)

    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


ploancp_df.head()


ploancp_df.tail()


ploancp_df.shape

(5000, 14)


print("The dataset has", ploancp_df.shape[0], "rows and",ploancp_df.shape[1],"columns.")
number_accounts = ploancp_df.shape[0]

The dataset has 5000 rows and 14 columns.


ploancp_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 14 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   ID                  5000 non-null   int64  
 1   Age                 5000 non-null   int64  
 2   Experience          5000 non-null   int64  
 3   Income              5000 non-null   int64  
 4   ZIPCode             5000 non-null   int64  
 5   Family              5000 non-null   int64  
 6   CCAvg               5000 non-null   float64
 7   Education           5000 non-null   int64  
 8   Mortgage            5000 non-null   int64  
 9   Personal_Loan       5000 non-null   int64  
 10  Securities_Account  5000 non-null   int64  
 11  CD_Account          5000 non-null   int64  
 12  Online              5000 non-null   int64  
 13  CreditCard          5000 non-null   int64  
dtypes: float64(1), int64(13)
memory usage: 547.0 KB


ploancp_df.describe().T


#Dropping ID
ploancp_df.drop('ID',axis=1,inplace=True)
ploancp_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 13 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Age                 5000 non-null   int64  
 1   Experience          5000 non-null   int64  
 2   Income              5000 non-null   int64  
 3   ZIPCode             5000 non-null   int64  
 4   Family              5000 non-null   int64  
 5   CCAvg               5000 non-null   float64
 6   Education           5000 non-null   int64  
 7   Mortgage            5000 non-null   int64  
 8   Personal_Loan       5000 non-null   int64  
 9   Securities_Account  5000 non-null   int64  
 10  CD_Account          5000 non-null   int64  
 11  Online              5000 non-null   int64  
 12  CreditCard          5000 non-null   int64  
dtypes: float64(1), int64(12)
memory usage: 507.9 KB


ploancp_df.isnull().values.any()

False


ploancp_df.isnull().sum()

Age                   0
Experience            0
Income                0
ZIPCode               0
Family                0
CCAvg                 0
Education             0
Mortgage              0
Personal_Loan         0
Securities_Account    0
CD_Account            0
Online                0
CreditCard            0
dtype: int64


cols_num2cat = ['ZIPCode','Education','Personal_Loan','Securities_Account','CD_Account','Online','CreditCard']
ploancp_df[cols_num2cat] = ploancp_df[cols_num2cat].astype('object')


numeric_cols = ploancp_df.select_dtypes(include=[np.number]).columns
cat_cols =ploancp_df.describe(include=["object"]).columns

print("Categorical Columns:     ", cat_cols)
print("Numeric Columns:    ", numeric_cols)

Categorical Columns:      Index(['ZIPCode', 'Education', 'Personal_Loan', 'Securities_Account',
       'CD_Account', 'Online', 'CreditCard'],
      dtype='object')
Numeric Columns:     Index(['Age', 'Experience', 'Income', 'Family', 'CCAvg', 'Mortgage'], dtype='object')


# Checking value counts of categorical variables
for i in cat_cols:
    print("*" * 50)
    print("Unique values in", i, "are :")
    print(ploancp_df[i].value_counts())
    print("-" * 50)
    print("Unique values Percentages", i, "are :")
    print(ploancp_df[i].value_counts(1)*100)

**************************************************
Unique values in ZIPCode are :
94720    169
94305    127
95616    116
90095     71
93106     57
        ... 
96145      1
94087      1
91024      1
93077      1
94598      1
Name: ZIPCode, Length: 467, dtype: int64
--------------------------------------------------
Unique values Percentages ZIPCode are :
94720   3.380
94305   2.540
95616   2.320
90095   1.420
93106   1.140
         ... 
96145   0.020
94087   0.020
91024   0.020
93077   0.020
94598   0.020
Name: ZIPCode, Length: 467, dtype: float64
**************************************************
Unique values in Education are :
1    2096
3    1501
2    1403
Name: Education, dtype: int64
--------------------------------------------------
Unique values Percentages Education are :
1   41.920
3   30.020
2   28.060
Name: Education, dtype: float64
**************************************************
Unique values in Personal_Loan are :
0    4520
1     480
Name: Personal_Loan, dtype: int64
--------------------------------------------------
Unique values Percentages Personal_Loan are :
0   90.400
1    9.600
Name: Personal_Loan, dtype: float64
**************************************************
Unique values in Securities_Account are :
0    4478
1     522
Name: Securities_Account, dtype: int64
--------------------------------------------------
Unique values Percentages Securities_Account are :
0   89.560
1   10.440
Name: Securities_Account, dtype: float64
**************************************************
Unique values in CD_Account are :
0    4698
1     302
Name: CD_Account, dtype: int64
--------------------------------------------------
Unique values Percentages CD_Account are :
0   93.960
1    6.040
Name: CD_Account, dtype: float64
**************************************************
Unique values in Online are :
1    2984
0    2016
Name: Online, dtype: int64
--------------------------------------------------
Unique values Percentages Online are :
1   59.680
0   40.320
Name: Online, dtype: float64
**************************************************
Unique values in CreditCard are :
0    3530
1    1470
Name: CreditCard, dtype: int64
--------------------------------------------------
Unique values Percentages CreditCard are :
0   70.600
1   29.400
Name: CreditCard, dtype: float64


# creating histograms for numeric_cols
ploancp_df[numeric_cols].hist(figsize=(14, 14))
plt.show()


#create barplots for categorical features

fig, axes = plt.subplots(2, 3, figsize=(15, 5), sharey=True)
fig.tight_layout(h_pad=2)
plt.subplots_adjust(top=0.89)
fig.suptitle('Distribution - Categorical Features')
sns.countplot(ax=axes[1, 1], data=ploancp_df, x='Personal_Loan')
sns.countplot(ax=axes[0, 2], data=ploancp_df, x='Securities_Account')
sns.countplot(ax=axes[0, 1], data=ploancp_df, x='CD_Account')
sns.countplot(ax=axes[0, 0], data=ploancp_df, x='Online')
sns.countplot(ax=axes[1, 0], data=ploancp_df, x='CreditCard')

plt.show()


#visualization Single Features as pie plots with percentage of total

fig, axes = plt.subplots(1, 7, figsize=(18, 18), sharey=True)

data_fy=ploancp_df['Family'].value_counts()
data_ed=ploancp_df['Education'].value_counts()
data_pl=ploancp_df['Personal_Loan'].value_counts()
data_sa =ploancp_df['Securities_Account'].value_counts()
data_cd = ploancp_df['CD_Account'].value_counts()
data_online =ploancp_df['Online'].value_counts()
data_cc = ploancp_df['CreditCard'].value_counts()

plt.title("Distribution - Single Feature")

ax1 = plt.subplot2grid((1,7), (0,0))
plt.pie(data_fy,labels=data_fy.index, autopct=autopct_format(data_fy),colors=("g","r"))
plt.title('Family')

ax1 = plt.subplot2grid((1,7), (0,1))
plt.pie(data_ed,labels=data_ed.index, autopct=autopct_format(data_ed),colors=("g","r"))
plt.title('Education')

ax1 = plt.subplot2grid((1,7),(0,2))
plt.pie(data_pl,labels=data_pl.index, autopct=autopct_format(data_pl),colors=("g","r"))
plt.title('Personal Loan')

ax1 = plt.subplot2grid((1,7), (0, 3))
plt.pie(data_sa,labels=data_sa.index, autopct=autopct_format(data_sa),colors=("g","r"))
plt.title('Securities Account')

ax1 = plt.subplot2grid((1,7), (0, 4))
plt.pie(data_cd,labels=data_cd.index, autopct=autopct_format(data_cd),colors=("g","r"))
plt.title('CD Account')

ax1 = plt.subplot2grid((1,7), (0, 5))
plt.pie(data_online,labels=data_online.index, autopct=autopct_format(data_online),colors=("g","r"))
plt.title('Online')

ax1 = plt.subplot2grid((1,7), (0, 6))
plt.pie(data_cc,labels=data_cc.index, autopct=autopct_format(data_cc),colors=("g","r"))
plt.title('Credit Card')

plt.show()


plt.figure(figsize=(6,4), dpi= 60)

plt.title("Age Distribution", fontsize=16)
sns.boxplot(data=ploancp_df,x='Age')
plt.show()


#same but as barplots
labeled_barplot(ploancp_df, 'Age', perc=True, n=None)


#How many Experience values are negative
num_exp_negative= ploancp_df.loc[ploancp_df['Experience']<0].value_counts().sum()
num_total_cust = ploan_df.shape[0]
print("The number of customers w/ apparent Negative experience is",num_exp_negative,"which represents",(num_exp_negative/num_total_cust)*100,"% of the total customers.")

The number of customers w/ apparent Negative experience is 52 which represents 1.04 % of the total customers.


#see the negative experience accounts
ploancp_df.loc[ploancp_df['Experience']<0]


#common Experience values and counts
ploancp_df['Experience'].value_counts()

 32    154
 20    148
 9     147
 5     146
 23    144
 35    143
 25    142
 28    138
 18    137
 19    135
 26    134
 24    131
 3     129
 16    127
 14    127
 30    126
 17    125
 34    125
 27    125
 22    124
 29    124
 7     121
 6     119
 15    119
 8     119
 10    118
 13    117
 33    117
 11    116
 37    116
 36    114
 21    113
 4     113
 31    104
 12    102
 38     88
 2      85
 39     85
 1      74
 0      66
 40     57
 41     43
-1      33
-2      15
 42      8
-3       4
 43      3
Name: Experience, dtype: int64


plt.figure(figsize=(6,4), dpi= 60)
plt.title("Positive Experience Distribution", fontsize=16)
sns.boxplot(data=ploancp_df.loc[ploancp_df['Experience']>=0],x='Experience')
plt.show()


plt.figure(figsize=(6,4), dpi= 60)

plt.title("Negative Experience Distribution", fontsize=16)
sns.boxplot(data=ploancp_df.loc[ploancp_df['Experience']<0],x='Experience')
plt.show()


#experience = -3
ploancp_x3_df = ploancp_df.loc[ploancp_df['Experience']==-3]
ploancp_x3_df


#experience = -2
ploancp_x2_df = ploancp_df.loc[ploancp_df['Experience']==-2]
ploancp_x2_df


#experience = -1
ploancp_x1_df = ploancp_df.loc[ploancp_df['Experience']==-1]
ploancp_x1_df


#experience = 0
ploancp_x0_df = ploancp_df.loc[ploancp_df['Experience']==0]
x0_median=ploancp_x0_df['Age'].median()
x0_min=ploancp_x0_df['Age'].min()
x0_max=ploancp_x0_df['Age'].max()

ploancp_x0_df


print("For Experience 0, The minimum age is",x0_min, ",the mean age is",x0_median,",the max age is",x0_max)

For Experience 0, The minimum age is 24 ,the mean age is 26.0 ,the max age is 30


#also for our plotting lets have a dataframe that includes all negative Experience.
ploancp_negEx_df = ploancp_df.loc[ploancp_df['Experience']<0]


plt.figure(figsize=(6,4), dpi= 60)
plt.title("Income Distribution", fontsize=16)
sns.boxplot(x='Income', data=ploancp_df, notch=False)
plt.show()


plt.figure(figsize=(6,3), dpi= 80)
sns.kdeplot(ploancp_df['Income'], shade=True, color="g", alpha=.7)
plt.title("Income Distribution")
plt.show()


income_min = ploancp_df['Income'].min()
income_max = ploancp_df['Income'].max()
income_mean = ploancp_df['Income'].median()
print("The Income minimum is", income_min, "K, the Income mean is", income_mean,"K, and the Income max is",income_max,"K")

The Income minimum is 8 K, the Income mean is 64.0 K, and the Income max is 224 K


bins = [0, 25, 50, 75, 100, 125, 150, 175, 200, 225]
labels = ['[0..25)','[25..50)','[50..75)', '[75..100)','[100..125)','[125..150)','[150..175)','[175..200)','[200..225]']
ploancp_df['Income_bin'] = pd.cut(ploancp_df['Income'], bins=bins, labels=labels)
print (ploancp_df)

      Age  Experience  Income ZIPCode  Family  CCAvg Education  Mortgage  \
0      25           1      49   91107       4  1.600         1         0   
1      45          19      34   90089       3  1.500         1         0   
2      39          15      11   94720       1  1.000         1         0   
3      35           9     100   94112       1  2.700         2         0   
4      35           8      45   91330       4  1.000         2         0   
...   ...         ...     ...     ...     ...    ...       ...       ...   
4995   29           3      40   92697       1  1.900         3         0   
4996   30           4      15   92037       4  0.400         1        85   
4997   63          39      24   93023       2  0.300         3         0   
4998   65          40      49   90034       3  0.500         2         0   
4999   28           4      83   92612       3  0.800         1         0   

     Personal_Loan Securities_Account CD_Account Online CreditCard Income_bin  
0                0                  1          0      0          0   [25..50)  
1                0                  1          0      0          0   [25..50)  
2                0                  0          0      0          0    [0..25)  
3                0                  0          0      0          0  [75..100)  
4                0                  0          0      0          1   [25..50)  
...            ...                ...        ...    ...        ...        ...  
4995             0                  0          0      1          0   [25..50)  
4996             0                  0          0      1          0    [0..25)  
4997             0                  0          0      0          0    [0..25)  
4998             0                  0          0      1          0   [25..50)  
4999             0                  0          0      1          1  [75..100)  

[5000 rows x 14 columns]


labeled_barplot(ploancp_df, 'Income_bin', perc=True, n=None)


#Show unique ZIPCodes.
ploancp_df['ZIPCode'].unique()

array([91107, 90089, 94720, 94112, 91330, 92121, 91711, 93943, 93023,
       94710, 90277, 93106, 94920, 91741, 95054, 95010, 94305, 91604,
       94015, 90095, 91320, 95521, 95064, 90064, 94539, 94104, 94117,
       94801, 94035, 92647, 95814, 94114, 94115, 92672, 94122, 90019,
       95616, 94065, 95014, 91380, 95747, 92373, 92093, 94005, 90245,
       95819, 94022, 90404, 93407, 94523, 90024, 91360, 95670, 95123,
       90045, 91335, 93907, 92007, 94606, 94611, 94901, 92220, 93305,
       95134, 94612, 92507, 91730, 94501, 94303, 94105, 94550, 92612,
       95617, 92374, 94080, 94608, 93555, 93311, 94704, 92717, 92037,
       95136, 94542, 94143, 91775, 92703, 92354, 92024, 92831, 92833,
       94304, 90057, 92130, 91301, 92096, 92646, 92182, 92131, 93720,
       90840, 95035, 93010, 94928, 95831, 91770, 90007, 94102, 91423,
       93955, 94107, 92834, 93117, 94551, 94596, 94025, 94545, 95053,
       90036, 91125, 95120, 94706, 95827, 90503, 90250, 95817, 95503,
       93111, 94132, 95818, 91942, 90401, 93524, 95133, 92173, 94043,
       92521, 92122, 93118, 92697, 94577, 91345, 94123, 92152, 91355,
       94609, 94306, 96150, 94110, 94707, 91326, 90291, 92807, 95051,
       94085, 92677, 92614, 92626, 94583, 92103, 92691, 92407, 90504,
       94002, 95039, 94063, 94923, 95023, 90058, 92126, 94118, 90029,
       92806, 94806, 92110, 94536, 90623, 92069, 92843, 92120, 95605,
       90740, 91207, 95929, 93437, 90630, 90034, 90266, 95630, 93657,
       92038, 91304, 92606, 92192, 90745, 95060, 94301, 92692, 92101,
       94610, 90254, 94590, 92028, 92054, 92029, 93105, 91941, 92346,
       94402, 94618, 94904, 93077, 95482, 91709, 91311, 94509, 92866,
       91745, 94111, 94309, 90073, 92333, 90505, 94998, 94086, 94709,
       95825, 90509, 93108, 94588, 91706, 92109, 92068, 95841, 92123,
       91342, 90232, 92634, 91006, 91768, 90028, 92008, 95112, 92154,
       92115, 92177, 90640, 94607, 92780, 90009, 92518, 91007, 93014,
       94024, 90027, 95207, 90717, 94534, 94010, 91614, 94234, 90210,
       95020, 92870, 92124, 90049, 94521, 95678, 95045, 92653, 92821,
       90025, 92835, 91910, 94701, 91129, 90071, 96651, 94960, 91902,
       90033, 95621, 90037, 90005, 93940, 91109, 93009, 93561, 95126,
       94109, 93107, 94591, 92251, 92648, 92709, 91754, 92009, 96064,
       91103, 91030, 90066, 95403, 91016, 95348, 91950, 95822, 94538,
       92056, 93063, 91040, 92661, 94061, 95758, 96091, 94066, 94939,
       95138, 95762, 92064, 94708, 92106, 92116, 91302, 90048, 90405,
       92325, 91116, 92868, 90638, 90747, 93611, 95833, 91605, 92675,
       90650, 95820, 90018, 93711, 95973, 92886, 95812, 91203, 91105,
       95008, 90016, 90035, 92129, 90720, 94949, 90041, 95003, 95192,
       91101, 94126, 90230, 93101, 91365, 91367, 91763, 92660, 92104,
       91361, 90011, 90032, 95354, 94546, 92673, 95741, 95351, 92399,
       90274, 94087, 90044, 94131, 94124, 95032, 90212, 93109, 94019,
       95828, 90086, 94555, 93033, 93022, 91343, 91911, 94803, 94553,
       95211, 90304, 92084, 90601, 92704, 92350, 94705, 93401, 90502,
       94571, 95070, 92735, 95037, 95135, 94028, 96003, 91024, 90065,
       95405, 95370, 93727, 92867, 95821, 94566, 95125, 94526, 94604,
       96008, 93065, 96001, 95006, 90639, 92630, 95307, 91801, 94302,
       91710, 93950, 90059, 94108, 94558, 93933, 92161, 94507, 94575,
       95449, 93403, 93460, 95005, 93302, 94040, 91401, 95816, 92624,
       95131, 94965, 91784, 91765, 90280, 95422, 95518, 95193, 92694,
       90275, 90272, 91791, 92705, 91773, 93003, 90755, 96145, 94703,
       96094, 95842, 94116, 90068, 94970, 90813, 94404, 94598],
      dtype=object)


numUniqueZips=ploancp_df['ZIPCode'].nunique()
print("The number of ZIPCodes is",numUniqueZips)

The number of ZIPCodes is 467


labeled_barplot(ploancp_df, 'ZIPCode', perc=True, n=None)


plt.figure(figsize=(6,4), dpi= 60)

plt.title("Family Size Distribution", fontsize=16)
sns.boxplot(data=ploancp_df,x='Family')
plt.show()


education_count = ploancp_df['Education'].value_counts()
education_count

1    2096
3    1501
2    1403
Name: Education, dtype: int64


#lets revisit the pie chart for Education, now with the proper labels
ed_df = ploancp_df['Education'].value_counts()
edlabels = {'UnderGraduate','Graduate','Advanced/Professional'}
plt.pie(ed_df,labels = edlabels, autopct=autopct_format(ed_df))
plt.title("Education Level Distribution")

plt.show()


plt.figure(figsize=(6,4), dpi= 60)
plt.title("Credit Card Average Usage Distribution", fontsize=16)
sns.boxplot(data=ploancp_df,x='CCAvg')
plt.show()


#Mortage Distribution
plt.figure(figsize=(6,4), dpi= 60)
plt.title("Mortgage Distribution", fontsize=16)
sns.boxplot(data=ploancp_df,x='Mortgage')
plt.show()


distribution_plot_wrt_target(ploancp_df, "Age", "Personal_Loan")


stacked_barplot(ploancp_df, "Age", "Personal_Loan")

Personal_Loan     0    1   All
Age                           
All            4520  480  5000
34              116   18   134
30              119   17   136
36               91   16   107
63               92   16   108
35              135   16   151
33              105   15   120
52              130   15   145
29              108   15   123
54              128   15   143
43              134   15   149
42              112   14   126
56              121   14   135
65               66   14    80
44              107   14   121
50              125   13   138
45              114   13   127
46              114   13   127
26               65   13    78
32              108   12   120
57              120   12   132
38              103   12   115
27               79   12    91
48              106   12   118
61              110   12   122
53              101   11   112
51              119   10   129
60              117   10   127
58              133   10   143
49              105   10   115
47              103   10   113
59              123    9   132
28               94    9   103
62              114    9   123
55              116    9   125
64               70    8    78
41              128    8   136
40              117    8   125
37               98    8   106
31              118    7   125
39              127    6   133
24               28    0    28
25               53    0    53
66               24    0    24
67               12    0    12
23               12    0    12
------------------------------------------------------------------------------------------------------------------------


#case to mode/median
Experience_mode = ploancp_df['Experience'].mode()
print("Experience mode is", Experience_mode[0])
Experience_median = ploancp_df['Experience'].median()
print("Experience median is",Experience_median)

Experience mode is 32
Experience median is 20.0


ploancp_negEx_df['modeX']= ploancp_negEx_df['Experience']-Experience_mode[0]
ploancp_negEx_df['medianX']= ploancp_negEx_df['Experience']-Experience_median


sns.displot(ploancp_negEx_df, x="modeX", kind="kde")
plt.xlabel("Age")
plt.title("Age minus Experience Mode")
plt.show()


sns.displot(ploancp_negEx_df, x="medianX", kind="kde")
plt.xlabel("Age")
plt.title("Age minus Experience Median")
plt.show()


ploancp_x0_df = ploancp_df.loc[ploancp_df['Experience']<0]
ploancp_x0_df['Experience'] = 0
ploancp_x0_df


#lets visualize age vs Experience when negative Experience is replaced by 0
sns.displot(ploancp_x0_df, x="Age", hue="Experience", kind="kde")
plt.title("Age vs Experience 0")
plt.show()


ploancp_x0_age_min_df = ploancp_x0_df['Age'].min()
ploancp_x0_age_median_df = ploancp_x0_df['Age'].median()
ploancp_x0_age_max_df = ploancp_x0_df['Age'].max()
print("For negative experience converted to 0, the minimum age is",ploancp_x0_age_min_df,
      ",the mean age is",ploancp_x0_age_median_df,
      ",the maximum age is",ploancp_x0_age_max_df)

For negative experience converted to 0, the minimum age is 23 ,the mean age is 24.0 ,the maximum age is 29


#make all experience year positive
ploancp_negEx_df['allPosX']= ploancp_negEx_df['Experience'].abs()
ploancp_negEx_df


#lets visualize age vs Experience when negative Experience is replaced by its positive value
sns.displot(ploancp_negEx_df, x="Age", hue="allPosX", kind="kde")
plt.title("Age vs Experience Positive for negatives")
plt.show()


#experience = 1
ploancp_x1_df = ploancp_df.loc[ploancp_df['Experience']==1]
x1_median=ploancp_x1_df['Age'].median()
x1_min=ploancp_x1_df['Age'].min()
x1_max=ploancp_x1_df['Age'].max()

ploancp_x1_df


print("For Experience 1, the minimum age is",x1_min,
      ",the mean age is",x1_median,
      ",the maximum age is",x1_max)

For Experience 1, the minimum age is 25 ,the mean age is 26.0 ,the maximum age is 31


#experience = 2
ploancp_x2_df = ploancp_df.loc[ploancp_df['Experience']==2]
x2_median=ploancp_x2_df['Age'].median()
x2_min=ploancp_x2_df['Age'].min()
x2_max=ploancp_x2_df['Age'].max()

ploancp_x2_df


print("For Experience 2, the minimum age is",x2_min,
      ",the mean age is",x2_median,
      ",the maximum age is",x2_max)

For Experience 2, the minimum age is 26 ,the mean age is 27.0 ,the maximum age is 32


#experience = 3
ploancp_x3_df = ploancp_df.loc[ploancp_df['Experience']==3]
x3_median=ploancp_x3_df['Age'].median()
x3_min=ploancp_x3_df['Age'].min()
x3_max=ploancp_x3_df['Age'].max()

ploancp_x3_df


print("For Experience 3, the minimum age is",x3_min,
      ",the mean age is",x3_median,
      ",the maximum age is",x3_max)

For Experience 3, the minimum age is 27 ,the mean age is 28.0 ,the maximum age is 33


#for converted negative experience to positive
#experience = 3
ploancp_NegX_3_df = ploancp_negEx_df.loc[ploancp_negEx_df['allPosX']==3]
NegX3_median=ploancp_NegX_3_df['Age'].median()
NegX3_min=ploancp_NegX_3_df['Age'].min()
NegX3_max=ploancp_NegX_3_df['Age'].max()
print("For Negative Experience -3 to +3, the minimum age is",NegX3_min,
      ",the mean age is",NegX3_median,
      ",the maximum age is",NegX3_max)

For Negative Experience -3 to +3, the minimum age is 23 ,the mean age is 23.5 ,the maximum age is 24


#for converted negative experience to positive
#experience = 2
ploancp_NegX_2_df = ploancp_negEx_df.loc[ploancp_negEx_df['allPosX']==2]
NegX2_median=ploancp_NegX_2_df['Age'].median()
NegX2_min=ploancp_NegX_2_df['Age'].min()
NegX2_max=ploancp_NegX_2_df['Age'].max()
print("For Negative Experience -2 to +2, the minimum age is",NegX2_min,
      ",the mean age is",NegX2_median,
      ",the maximum age is",NegX2_max)

For Negative Experience -2 to +2, the minimum age is 23 ,the mean age is 24.0 ,the maximum age is 28


#for converted negative experience to positive
#experience = 1
ploancp_NegX_1_df = ploancp_negEx_df.loc[ploancp_negEx_df['allPosX']==1]
NegX1_median=ploancp_NegX_1_df['Age'].median()
NegX1_min=ploancp_NegX_1_df['Age'].min()
NegX1_max=ploancp_NegX_1_df['Age'].max()
print("For Negative Experience -1 to +1, the minimum age is",NegX1_min,
      ",the mean age is",NegX1_median,
      ",the maximum age is",NegX1_max)

For Negative Experience -1 to +1, the minimum age is 23 ,the mean age is 25.0 ,the maximum age is 29


ploancp_df.loc[ploancp_df['Experience'] < 0, 'Experience'] = 0


distribution_plot_wrt_target(ploancp_df, "Experience", "Personal_Loan")


distribution_plot_wrt_target(ploancp_df, "Income", "Personal_Loan")


stacked_barplot(ploancp_df, "Income", "Personal_Loan")

Personal_Loan     0    1   All
Income                        
All            4520  480  5000
130               8   11    19
182               2   11    13
158               8   10    18
135               8   10    18
179               8    9    17
141              15    9    24
154              12    9    21
123               9    9    18
184               3    9    12
142               7    8    15
131              11    8    19
129              15    8    23
172               3    8    11
173               5    8    13
170               4    8    12
180              10    8    18
115              19    8    27
125              16    7    23
164               6    7    13
188               3    7    10
83               67    7    74
114              23    7    30
161               9    7    16
122              17    7    24
133               8    7    15
132              11    7    18
191               6    7    13
134              13    7    20
111              15    7    22
190               4    7    11
145              17    6    23
140              13    6    19
178               4    6    10
118              13    6    19
185               3    6     9
165               5    6    11
168               2    6     8
169               1    6     7
183               6    6    12
120              11    6    17
139              10    6    16
113              29    5    34
119              13    5    18
99               19    5    24
138              13    5    18
155              14    5    19
195              10    5    15
174               4    5     9
175               7    5    12
152              10    5    15
153               7    4    11
181               4    4     8
103              14    4    18
93               33    4    37
108              12    4    16
101              20    4    24
194               4    4     8
192               2    4     6
193               2    4     6
143               5    4     9
149              16    4    20
171               5    4     9
160               8    4    12
159               3    4     7
128              20    4    24
148               7    4    11
162               7    3    10
112              23    3    26
110              16    3    19
124               9    3    12
105              17    3    20
104              17    3    20
102              13    3    16
109              15    3    18
95               22    3    25
150               9    2    11
94               24    2    26
163               7    2     9
91               35    2    37
98               26    2    28
89               32    2    34
121              18    2    20
85               63    2    65
144               5    2     7
65               59    1    60
71               42    1    43
69               45    1    46
100               9    1    10
60               51    1    52
189               1    1     2
73               43    1    44
151               3    1     4
201               4    1     5
64               59    1    60
202               1    1     2
81               82    1    83
92               28    1    29
90               37    1    38
75               46    1    47
82               60    1    61
84               62    1    63
203               1    1     2
33               51    0    51
198               3    0     3
31               55    0    55
30               63    0    63
29               67    0    67
28               63    0    63
25               64    0    64
24               47    0    47
23               54    0    54
22               65    0    65
224               1    0     1
21               65    0    65
20               47    0    47
19               52    0    52
200               3    0     3
218               1    0     1
18               53    0    53
204               3    0     3
15               33    0    33
199               3    0     3
14               31    0    31
13               32    0    32
12               30    0    30
205               2    0     2
11               27    0    27
10               23    0    23
32               58    0    58
48               44    0    44
34               53    0    53
35               65    0    65
9                26    0    26
88               26    0    26
80               56    0    56
79               53    0    53
78               61    0    61
74               45    0    45
72               41    0    41
70               47    0    47
68               35    0    35
63               46    0    46
62               55    0    55
61               57    0    57
59               53    0    53
58               55    0    55
55               61    0    61
54               52    0    52
53               57    0    57
52               47    0    47
51               41    0    41
50               45    0    45
49               52    0    52
45               69    0    69
44               85    0    85
43               70    0    70
42               77    0    77
41               82    0    82
40               78    0    78
39               81    0    81
38               84    0    84
8                23    0    23
------------------------------------------------------------------------------------------------------------------------


#for the number of accounts
ziploan = ploancp_df[['ZIPCode','Personal_Loan']]
ziploan_count = ziploan['ZIPCode'].value_counts()
ziploan_count_df= pd.DataFrame(ziploan_count)
ziploan_count_df.reset_index(inplace=True)
ziploan_count_df = ziploan_count_df.rename(columns={'ZIPCode': 'NoAccounts', 'index': 'ZIPCode'})
ziploan_count_descending_df = ziploan_count_df.sort_values(['NoAccounts'],ascending=False)
ziploan_count_descending_df


ziploan_count_descending_df.head(10)


#for the number of loans
ziploanpl = ploancp_df[ploancp_df['Personal_Loan']== True]
number_loans = ziploanpl['Personal_Loan'].value_counts()
ziploanpl_count = ziploanpl['ZIPCode'].value_counts()
ziploanpl_count_df = pd.DataFrame(ziploanpl_count)
ziploanpl_count_df.reset_index(inplace=True)
ziploanpl_count_df = ziploanpl_count_df.rename(columns={'ZIPCode': 'PLoans', 'index': 'ZIPCode'})


#calculating the percentages
zipLoanTrue = pd.merge(ziploan_count_df, ziploanpl_count_df, on ='ZIPCode')
zipLoanTrue['PercentLoans'] = (zipLoanTrue['PLoans']/number_loans[1])
zipLoanTrue['PercentAccounts']= (zipLoanTrue['NoAccounts']/number_accounts)

#sort in descending order for number of Loans
zipLoanTrue_descending_df = zipLoanTrue.sort_values(['PLoans'],ascending=False)
zipLoanTrue_descending_df.head(20)


#sort in descending order for % of accounts out of total number
zipLoanTrue_descending_df = zipLoanTrue.sort_values(['PercentAccounts'],ascending=False)
zipLoanTrue_descending_df.head(50)


zipLoanTrue_descending_loan2_df = zipLoanTrue_descending_df.loc[zipLoanTrue_descending_df['PLoans']>=2]
zipLoanTrue_descending_loan2_df


distribution_plot_wrt_target(ploancp_df, "Family", "Personal_Loan")


stacked_barplot(ploancp_df, "Family", "Personal_Loan")

Personal_Loan     0    1   All
Family                        
All            4520  480  5000
4              1088  134  1222
3               877  133  1010
1              1365  107  1472
2              1190  106  1296
------------------------------------------------------------------------------------------------------------------------


#lets create a new column to represent the % of CCAVg per month over Average monthly Income.
ploancp_df['CCAvg_Inc%']=(ploancp_df['CCAvg']*100/(ploancp_df['Income']/12))
ploancp_df['CCAvg_Inc%']

0       39.184
1       52.941
2      109.091
3       32.400
4       26.667
         ...  
4995    57.000
4996    32.000
4997    15.000
4998    12.245
4999    11.566
Name: CCAvg_Inc%, Length: 5000, dtype: float64


ploancp_df['CCAvg_Inc%'].value_counts()
ploancp_df['CCAvg_Inc%'].max()
ploancp_df['CCAvg_Inc%'].min()

0.0


plt.figure(figsize=(6,4), dpi= 60)
plt.title("Credit Card Average Usage % of Income Distribution ", fontsize=16)
sns.boxplot(data=ploancp_df,x='CCAvg_Inc%')
plt.show()


ploancp_df.drop('CCAvg', axis=1, inplace=True)


bins = [-0.1, 0, 10, 20, 30,40, 50, 60,70,80, 90, 100, 110, 120, 130, 140, 150]
labels = [0, 10, 20, 30,40, 50, 60,70,80, 90, 100, 110, 120, 130, 140, 150]
ploancp_df['CCAvg_Inc%_bin'] = pd.cut(ploancp_df['CCAvg_Inc%'], bins=bins, labels=labels)
print (ploancp_df)

      Age  Experience  Income ZIPCode  Family Education  Mortgage  \
0      25           1      49   91107       4         1         0   
1      45          19      34   90089       3         1         0   
2      39          15      11   94720       1         1         0   
3      35           9     100   94112       1         2         0   
4      35           8      45   91330       4         2         0   
...   ...         ...     ...     ...     ...       ...       ...   
4995   29           3      40   92697       1         3         0   
4996   30           4      15   92037       4         1        85   
4997   63          39      24   93023       2         3         0   
4998   65          40      49   90034       3         2         0   
4999   28           4      83   92612       3         1         0   

     Personal_Loan Securities_Account CD_Account Online CreditCard Income_bin  \
0                0                  1          0      0          0   [25..50)   
1                0                  1          0      0          0   [25..50)   
2                0                  0          0      0          0    [0..25)   
3                0                  0          0      0          0  [75..100)   
4                0                  0          0      0          1   [25..50)   
...            ...                ...        ...    ...        ...        ...   
4995             0                  0          0      1          0   [25..50)   
4996             0                  0          0      1          0    [0..25)   
4997             0                  0          0      0          0    [0..25)   
4998             0                  0          0      1          0   [25..50)   
4999             0                  0          0      1          1  [75..100)   

      CCAvg_Inc% CCAvg_Inc%_bin  
0         39.184             40  
1         52.941             60  
2        109.091            110  
3         32.400             40  
4         26.667             30  
...          ...            ...  
4995      57.000             60  
4996      32.000             40  
4997      15.000             20  
4998      12.245             20  
4999      11.566             20  

[5000 rows x 15 columns]


ploancp_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype   
---  ------              --------------  -----   
 0   Age                 5000 non-null   int64   
 1   Experience          5000 non-null   int64   
 2   Income              5000 non-null   int64   
 3   ZIPCode             5000 non-null   object  
 4   Family              5000 non-null   int64   
 5   Education           5000 non-null   object  
 6   Mortgage            5000 non-null   int64   
 7   Personal_Loan       5000 non-null   object  
 8   Securities_Account  5000 non-null   object  
 9   CD_Account          5000 non-null   object  
 10  Online              5000 non-null   object  
 11  CreditCard          5000 non-null   object  
 12  Income_bin          5000 non-null   category
 13  CCAvg_Inc%          5000 non-null   float64 
 14  CCAvg_Inc%_bin      5000 non-null   category
dtypes: category(2), float64(1), int64(5), object(7)
memory usage: 518.7+ KB


distribution_plot_wrt_target(ploancp_df, "CCAvg_Inc%","Personal_Loan")


distribution_plot_wrt_target(ploancp_df, "CCAvg_Inc%_bin","Personal_Loan")


stacked_barplot(ploancp_df, "CCAvg_Inc%_bin", "Personal_Loan")

Personal_Loan      0    1   All
CCAvg_Inc%_bin                 
All             4520  480  5000
50               590  111   701
60               526   88   614
30               785   80   865
40               764   77   841
10               562   60   622
20               727   60   787
70               227    3   230
0                105    1   106
80               146    0   146
90                48    0    48
100               25    0    25
110                7    0     7
120                3    0     3
140                4    0     4
150                1    0     1
------------------------------------------------------------------------------------------------------------------------


distribution_plot_wrt_target(ploancp_df, "Education", "Personal_Loan")


stacked_barplot(ploancp_df, "Education", "Personal_Loan")

Personal_Loan     0    1   All
Education                     
All            4520  480  5000
3              1296  205  1501
2              1221  182  1403
1              2003   93  2096
------------------------------------------------------------------------------------------------------------------------


#lets create a new column to represent the % of Mortage over yearly Income.
ploancp_df['Mortgage_Inc%']=(ploancp_df['Mortgage']*100/ploancp_df['Income'])
ploancp_df['Mortgage_Inc%']

0        0.000
1        0.000
2        0.000
3        0.000
4        0.000
         ...  
4995     0.000
4996   566.667
4997     0.000
4998     0.000
4999     0.000
Name: Mortgage_Inc%, Length: 5000, dtype: float64


#Mortage Distribution as a % of Yearly Income
plt.figure(figsize=(6,4), dpi= 60)
plt.title("Total Mortgage as % of Yearly Income Distribution", fontsize=16)
sns.boxplot(data=ploancp_df,x='Mortgage_Inc%')
plt.show()


#lets create a new column to represent the % of yearly Mortage over yearly Income.
ploancp_df['year_Mortgage_Inc%']=((ploancp_df['Mortgage']/30)*100/ploancp_df['Income'])
ploancp_df['year_Mortgage_Inc%']

0       0.000
1       0.000
2       0.000
3       0.000
4       0.000
        ...  
4995    0.000
4996   18.889
4997    0.000
4998    0.000
4999    0.000
Name: year_Mortgage_Inc%, Length: 5000, dtype: float64


#Yearly Mortage Distribution as a % of Yearly Income
plt.figure(figsize=(6,4), dpi= 60)
plt.title("Yearly Mortgage Amortization as % Yearly Income Distribution", fontsize=16)
sns.boxplot(data=ploancp_df,x='year_Mortgage_Inc%')
plt.show()


distribution_plot_wrt_target(ploancp_df, "year_Mortgage_Inc%","Personal_Loan")


bins = [-0.1,0, 5, 10, 15, 20,25, 30, 35,40,45]
labels = [0, 5, 10, 15, 20,25, 30, 35,40,45]
ploancp_df['year_Mortage_Inc%_bin'] = pd.cut(ploancp_df['year_Mortgage_Inc%'], bins=bins, labels=labels)
print (ploancp_df)

      Age  Experience  Income ZIPCode  Family Education  Mortgage  \
0      25           1      49   91107       4         1         0   
1      45          19      34   90089       3         1         0   
2      39          15      11   94720       1         1         0   
3      35           9     100   94112       1         2         0   
4      35           8      45   91330       4         2         0   
...   ...         ...     ...     ...     ...       ...       ...   
4995   29           3      40   92697       1         3         0   
4996   30           4      15   92037       4         1        85   
4997   63          39      24   93023       2         3         0   
4998   65          40      49   90034       3         2         0   
4999   28           4      83   92612       3         1         0   

     Personal_Loan Securities_Account CD_Account Online CreditCard Income_bin  \
0                0                  1          0      0          0   [25..50)   
1                0                  1          0      0          0   [25..50)   
2                0                  0          0      0          0    [0..25)   
3                0                  0          0      0          0  [75..100)   
4                0                  0          0      0          1   [25..50)   
...            ...                ...        ...    ...        ...        ...   
4995             0                  0          0      1          0   [25..50)   
4996             0                  0          0      1          0    [0..25)   
4997             0                  0          0      0          0    [0..25)   
4998             0                  0          0      1          0   [25..50)   
4999             0                  0          0      1          1  [75..100)   

      CCAvg_Inc% CCAvg_Inc%_bin  Mortgage_Inc%  year_Mortgage_Inc%  \
0         39.184             40          0.000               0.000   
1         52.941             60          0.000               0.000   
2        109.091            110          0.000               0.000   
3         32.400             40          0.000               0.000   
4         26.667             30          0.000               0.000   
...          ...            ...            ...                 ...   
4995      57.000             60          0.000               0.000   
4996      32.000             40        566.667              18.889   
4997      15.000             20          0.000               0.000   
4998      12.245             20          0.000               0.000   
4999      11.566             20          0.000               0.000   

     year_Mortage_Inc%_bin  
0                        0  
1                        0  
2                        0  
3                        0  
4                        0  
...                    ...  
4995                     0  
4996                    20  
4997                     0  
4998                     0  
4999                     0  

[5000 rows x 18 columns]


#ploancp_df['year_Mortage_Inc%_bin']
ploancp_df['year_Mortage_Inc%_bin'] = ploancp_df['year_Mortage_Inc%_bin'].astype('int')


distribution_plot_wrt_target(ploancp_df, "year_Mortage_Inc%_bin","Personal_Loan")


sns.countplot(ploancp_df["year_Mortage_Inc%_bin"])
plt.title("Mortgage Distribution as Yearly Income % - 5% bins")
plt.show()


ploancp_df.drop(['Mortgage','Mortgage_Inc%'], axis=1, inplace=True)


#lets look at the mortage
ploancp_df['year_Mortgage_Inc%'].value_counts()

0.000     3462
10.000       5
14.545       5
11.429       4
4.667        4
          ... 
5.805        1
10.000       1
24.524       1
10.397       1
18.889       1
Name: year_Mortgage_Inc%, Length: 1392, dtype: int64


ploancp_df['year_Mortgage_Inc%'].max()
ploancp_df['year_Mortgage_Inc%'].min()

0.0


stacked_barplot(ploancp_df, "year_Mortage_Inc%_bin", "Personal_Loan")

Personal_Loan             0    1   All
year_Mortage_Inc%_bin                 
All                    4520  480  5000
0                      3150  312  3462
10                      504   77   581
5                       149   62   211
15                      484   29   513
20                      143    0   143
25                       43    0    43
30                       26    0    26
35                       12    0    12
40                        7    0     7
45                        2    0     2
------------------------------------------------------------------------------------------------------------------------


distribution_plot_wrt_target(ploancp_df, "Securities_Account", "Personal_Loan")


stacked_barplot(ploancp_df, "Securities_Account", "Personal_Loan")

Personal_Loan          0    1   All
Securities_Account                 
All                 4520  480  5000
0                   4058  420  4478
1                    462   60   522
------------------------------------------------------------------------------------------------------------------------


distribution_plot_wrt_target(ploancp_df, "CD_Account", "Personal_Loan")


stacked_barplot(ploancp_df, "CD_Account", "Personal_Loan")

Personal_Loan     0    1   All
CD_Account                    
All            4520  480  5000
0              4358  340  4698
1               162  140   302
------------------------------------------------------------------------------------------------------------------------


distribution_plot_wrt_target(ploancp_df, "Online", "Personal_Loan")


stacked_barplot(ploancp_df, "Online", "Personal_Loan")

Personal_Loan     0    1   All
Online                        
All            4520  480  5000
1              2693  291  2984
0              1827  189  2016
------------------------------------------------------------------------------------------------------------------------


distribution_plot_wrt_target(ploancp_df, "CreditCard", "Personal_Loan")


stacked_barplot(ploancp_df, "CreditCard", "Personal_Loan")

Personal_Loan     0    1   All
CreditCard                    
All            4520  480  5000
0              3193  337  3530
1              1327  143  1470
------------------------------------------------------------------------------------------------------------------------


ploancp_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 16 columns):
 #   Column                 Non-Null Count  Dtype   
---  ------                 --------------  -----   
 0   Age                    5000 non-null   int64   
 1   Experience             5000 non-null   int64   
 2   Income                 5000 non-null   int64   
 3   ZIPCode                5000 non-null   object  
 4   Family                 5000 non-null   int64   
 5   Education              5000 non-null   object  
 6   Personal_Loan          5000 non-null   object  
 7   Securities_Account     5000 non-null   object  
 8   CD_Account             5000 non-null   object  
 9   Online                 5000 non-null   object  
 10  CreditCard             5000 non-null   object  
 11  Income_bin             5000 non-null   category
 12  CCAvg_Inc%             5000 non-null   float64 
 13  CCAvg_Inc%_bin         5000 non-null   category
 14  year_Mortgage_Inc%     5000 non-null   float64 
 15  year_Mortage_Inc%_bin  5000 non-null   int64   
dtypes: category(2), float64(2), int64(5), object(7)
memory usage: 557.8+ KB


ploancp_df.drop(['year_Mortage_Inc%_bin', 'CCAvg_Inc%_bin', 'Income_bin'], axis=1,inplace=True)


ploancp_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 13 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Age                 5000 non-null   int64  
 1   Experience          5000 non-null   int64  
 2   Income              5000 non-null   int64  
 3   ZIPCode             5000 non-null   object 
 4   Family              5000 non-null   int64  
 5   Education           5000 non-null   object 
 6   Personal_Loan       5000 non-null   object 
 7   Securities_Account  5000 non-null   object 
 8   CD_Account          5000 non-null   object 
 9   Online              5000 non-null   object 
 10  CreditCard          5000 non-null   object 
 11  CCAvg_Inc%          5000 non-null   float64
 12  year_Mortgage_Inc%  5000 non-null   float64
dtypes: float64(2), int64(4), object(7)
memory usage: 507.9+ KB


numerical_col = ploancp_df.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(ploancp_df[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


ploancp_df.dtypes

Age                     int64
Experience              int64
Income                  int64
ZIPCode                object
Family                  int64
Education              object
Personal_Loan          object
Securities_Account     object
CD_Account             object
Online                 object
CreditCard             object
CCAvg_Inc%            float64
year_Mortgage_Inc%    float64
dtype: object


cols_num2cat = ['Education','Personal_Loan','Securities_Account','CD_Account','Online','CreditCard','CCAvg_Inc%','year_Mortgage_Inc%']
ploancp_df[cols_num2cat] = ploancp_df[cols_num2cat].astype('int')


fig, ax = plt.subplots(figsize=(12,10))
ax=sns.heatmap(ploancp_df.corr(), cmap="YlGnBu", annot=True)


ploancp_df= pd.get_dummies(ploancp_df, columns = ['ZIPCode'])


ploancp_df.head()


ploancp_df.dtypes

Age              int64
Experience       int64
Income           int64
Family           int64
Education        int64
                 ...  
ZIPCode_96091    uint8
ZIPCode_96094    uint8
ZIPCode_96145    uint8
ZIPCode_96150    uint8
ZIPCode_96651    uint8
Length: 479, dtype: object


X = ploancp_df.drop(["Personal_Loan"], axis=1)
Y = ploancp_df["Personal_Loan"]

X = pd.get_dummies(X, drop_first=True)

X


X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1
)


print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set :  (3500, 478)
Shape of test set :  (1500, 478)
Percentage of classes in training set:
0   0.905
1   0.095
Name: Personal_Loan, dtype: float64
Percentage of classes in test set:
0   0.901
1   0.099
Name: Personal_Loan, dtype: float64


X_train


y_train

1334    0
4768    0
65      0
177     0
4489    0
       ..
2895    0
2763    0
905     0
3980    0
235     0
Name: Personal_Loan, Length: 3500, dtype: int64


y_test

2764    0
4767    0
3814    0
3499    0
2735    0
       ..
4140    0
3969    0
2535    0
1361    0
1458    0
Name: Personal_Loan, Length: 1500, dtype: int64


# There are different solvers available in Sklearn logistic regression
# The newton-cg solver is faster for high-dimensional data
lg = LogisticRegression(solver="newton-cg", random_state=1)
model = lg.fit(X_train, y_train)


# creating confusion matrix for training sets
confusion_matrix_sklearn_with_threshold(lg, X_train, y_train)


log_reg_model_train_perf = model_performance_classification_sklearn_with_threshold(
    lg, X_train, y_train
)

print("Training performance:")
log_reg_model_train_perf

Training performance:


# creating confusion matrix for testing sets
confusion_matrix_sklearn_with_threshold(lg, X_test, y_test)


log_reg_model_test_perf = model_performance_classification_sklearn_with_threshold(
    lg, X_test, y_test
)

print("Test set performance:")
log_reg_model_test_perf

Test set performance:


# Find the roc auc score for training data
logit_roc_auc_train = roc_auc_score(
    y_train, lg.predict_proba(X_train)[:, 1]
)  # The indexing represents predicted probabilities for class 1
# Find fpr, tpr and threshold values
fpr, tpr, thresholds = roc_curve(y_train, lg.predict_proba(X_train)[:, 1])
plt.figure(figsize=(7, 5))
# Plot roc curve
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# Find the roc auc score for testing data
logit_roc_auc_test = roc_auc_score(
    y_test, lg.predict_proba(X_test)[:, 1]
)  # The indexing represents predicted probabilities for class 1
# Find fpr, tpr and threshold values
fpr, tpr, thresholds = roc_curve(y_test, lg.predict_proba(X_test)[:, 1])
plt.figure(figsize=(7, 5))
# Plot roc curve
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_test)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
# roc_curve returns the fpr, tpr and threshold values which takes the original data and predicted probabilities for the class 1.
fpr, tpr, thresholds = roc_curve(
    y_train, lg.predict_proba(X_train)[:, 1]
)  # The indexing represents predicted probabilities for class 1

optimal_idx = np.argmax(
    tpr - fpr
)  # Finds the index that contains the max difference between tpr and fpr
optimal_threshold_auc_roc = thresholds[
    optimal_idx
]  # stores the optimal threshold value
print(optimal_threshold_auc_roc)

0.1462260641989123


# creating confusion matrix
confusion_matrix_sklearn_with_threshold(
    lg, X_train, y_train, threshold=optimal_threshold_auc_roc
)


# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_sklearn_with_threshold(
    lg, X_train, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc

Training performance:


# creating confusion matrix on test set
confusion_matrix_sklearn_with_threshold(
    lg, X_test, y_test, threshold=optimal_threshold_auc_roc
)


# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_sklearn_with_threshold(
    lg, X_test, y_test, threshold=optimal_threshold_auc_roc
)
print("Test set performance:")
log_reg_model_test_perf_threshold_auc_roc

Test set performance:


# Find the predicted probabilities for class 1
y_scores = lg.predict_proba(X_train)[:, 1]
# Find fpr, tpr and threshold values
prec, rec, tre = precision_recall_curve(y_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
# Plot recall precision curve
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()


# setting the threshold
optimal_threshold_curve = 0.37


# creating confusion matrix
confusion_matrix_sklearn_with_threshold(
    lg, X_train, y_train, threshold=optimal_threshold_curve
)


log_reg_model_train_perf_threshold_curve = model_performance_classification_sklearn_with_threshold(
    lg, X_train, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve

Training performance:


# creating confusion matrix
confusion_matrix_sklearn_with_threshold(
    lg, X_test, y_test, threshold=optimal_threshold_curve
)


log_reg_model_test_perf_threshold_curve = model_performance_classification_sklearn_with_threshold(
    lg, X_test, y_test, threshold=optimal_threshold_curve
)
print("Test set performance:")
log_reg_model_test_perf_threshold_curve

Test set performance:


# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression sklearn",
    "Logistic Regression-0.15 Threshold",
    "Logistic Regression-0.37 Threshold",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# testing performance comparison

models_test_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression sklearn",
    "Logistic Regression-0.15 Threshold",
    "Logistic Regression-0.37 Threshold",
]
print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:


from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
import sklearn.metrics as metrics


dTree = DecisionTreeClassifier(criterion = 'gini', random_state=1)
dTree.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)


#scoring
print("Accuracy on training set : ",dTree.score(X_train, y_train))
print("Accuracy on test set : ",dTree.score(X_test, y_test))

Accuracy on training set :  1.0
Accuracy on test set :  0.9746666666666667


## Function to create confusion matrix
def make_confusion_matrix(model,y_actual,labels=[1, 0]):
    '''
    model : classifier to predict values of X
    y_actual : ground truth

    '''
    y_predict = model.predict(X_test)
    cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
    df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                  columns = [i for i in ['Predicted - No','Predicted - Yes']])
    group_counts = ["{0:0.0f}".format(value) for value in
                cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in
                         cm.flatten()/np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in
              zip(group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    plt.figure(figsize = (10,7))
    sns.heatmap(df_cm, annot=labels,fmt='')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


##  Function to calculate recall score
def get_recall_score(model):
    '''
    model : classifier to predict values of X

    '''
    pred_train = model.predict(X_train)
    pred_test = model.predict(X_test)
    print("Recall on training set : ",metrics.recall_score(y_train,pred_train))
    print("Recall on test set : ",metrics.recall_score(y_test,pred_test))


make_confusion_matrix(dTree,y_test)


# Recall on train and test
get_recall_score(dTree)

Recall on training set :  1.0
Recall on test set :  0.8322147651006712


feature_names = list(X.columns)
print(feature_names)

['Age', 'Experience', 'Income', 'Family', 'Education', 'Securities_Account', 'CD_Account', 'Online', 'CreditCard', 'CCAvg_Inc%', 'year_Mortgage_Inc%', 'ZIPCode_90005', 'ZIPCode_90007', 'ZIPCode_90009', 'ZIPCode_90011', 'ZIPCode_90016', 'ZIPCode_90018', 'ZIPCode_90019', 'ZIPCode_90024', 'ZIPCode_90025', 'ZIPCode_90027', 'ZIPCode_90028', 'ZIPCode_90029', 'ZIPCode_90032', 'ZIPCode_90033', 'ZIPCode_90034', 'ZIPCode_90035', 'ZIPCode_90036', 'ZIPCode_90037', 'ZIPCode_90041', 'ZIPCode_90044', 'ZIPCode_90045', 'ZIPCode_90048', 'ZIPCode_90049', 'ZIPCode_90057', 'ZIPCode_90058', 'ZIPCode_90059', 'ZIPCode_90064', 'ZIPCode_90065', 'ZIPCode_90066', 'ZIPCode_90068', 'ZIPCode_90071', 'ZIPCode_90073', 'ZIPCode_90086', 'ZIPCode_90089', 'ZIPCode_90095', 'ZIPCode_90210', 'ZIPCode_90212', 'ZIPCode_90230', 'ZIPCode_90232', 'ZIPCode_90245', 'ZIPCode_90250', 'ZIPCode_90254', 'ZIPCode_90266', 'ZIPCode_90272', 'ZIPCode_90274', 'ZIPCode_90275', 'ZIPCode_90277', 'ZIPCode_90280', 'ZIPCode_90291', 'ZIPCode_90304', 'ZIPCode_90401', 'ZIPCode_90404', 'ZIPCode_90405', 'ZIPCode_90502', 'ZIPCode_90503', 'ZIPCode_90504', 'ZIPCode_90505', 'ZIPCode_90509', 'ZIPCode_90601', 'ZIPCode_90623', 'ZIPCode_90630', 'ZIPCode_90638', 'ZIPCode_90639', 'ZIPCode_90640', 'ZIPCode_90650', 'ZIPCode_90717', 'ZIPCode_90720', 'ZIPCode_90740', 'ZIPCode_90745', 'ZIPCode_90747', 'ZIPCode_90755', 'ZIPCode_90813', 'ZIPCode_90840', 'ZIPCode_91006', 'ZIPCode_91007', 'ZIPCode_91016', 'ZIPCode_91024', 'ZIPCode_91030', 'ZIPCode_91040', 'ZIPCode_91101', 'ZIPCode_91103', 'ZIPCode_91105', 'ZIPCode_91107', 'ZIPCode_91109', 'ZIPCode_91116', 'ZIPCode_91125', 'ZIPCode_91129', 'ZIPCode_91203', 'ZIPCode_91207', 'ZIPCode_91301', 'ZIPCode_91302', 'ZIPCode_91304', 'ZIPCode_91311', 'ZIPCode_91320', 'ZIPCode_91326', 'ZIPCode_91330', 'ZIPCode_91335', 'ZIPCode_91342', 'ZIPCode_91343', 'ZIPCode_91345', 'ZIPCode_91355', 'ZIPCode_91360', 'ZIPCode_91361', 'ZIPCode_91365', 'ZIPCode_91367', 'ZIPCode_91380', 'ZIPCode_91401', 'ZIPCode_91423', 'ZIPCode_91604', 'ZIPCode_91605', 'ZIPCode_91614', 'ZIPCode_91706', 'ZIPCode_91709', 'ZIPCode_91710', 'ZIPCode_91711', 'ZIPCode_91730', 'ZIPCode_91741', 'ZIPCode_91745', 'ZIPCode_91754', 'ZIPCode_91763', 'ZIPCode_91765', 'ZIPCode_91768', 'ZIPCode_91770', 'ZIPCode_91773', 'ZIPCode_91775', 'ZIPCode_91784', 'ZIPCode_91791', 'ZIPCode_91801', 'ZIPCode_91902', 'ZIPCode_91910', 'ZIPCode_91911', 'ZIPCode_91941', 'ZIPCode_91942', 'ZIPCode_91950', 'ZIPCode_92007', 'ZIPCode_92008', 'ZIPCode_92009', 'ZIPCode_92024', 'ZIPCode_92028', 'ZIPCode_92029', 'ZIPCode_92037', 'ZIPCode_92038', 'ZIPCode_92054', 'ZIPCode_92056', 'ZIPCode_92064', 'ZIPCode_92068', 'ZIPCode_92069', 'ZIPCode_92084', 'ZIPCode_92093', 'ZIPCode_92096', 'ZIPCode_92101', 'ZIPCode_92103', 'ZIPCode_92104', 'ZIPCode_92106', 'ZIPCode_92109', 'ZIPCode_92110', 'ZIPCode_92115', 'ZIPCode_92116', 'ZIPCode_92120', 'ZIPCode_92121', 'ZIPCode_92122', 'ZIPCode_92123', 'ZIPCode_92124', 'ZIPCode_92126', 'ZIPCode_92129', 'ZIPCode_92130', 'ZIPCode_92131', 'ZIPCode_92152', 'ZIPCode_92154', 'ZIPCode_92161', 'ZIPCode_92173', 'ZIPCode_92177', 'ZIPCode_92182', 'ZIPCode_92192', 'ZIPCode_92220', 'ZIPCode_92251', 'ZIPCode_92325', 'ZIPCode_92333', 'ZIPCode_92346', 'ZIPCode_92350', 'ZIPCode_92354', 'ZIPCode_92373', 'ZIPCode_92374', 'ZIPCode_92399', 'ZIPCode_92407', 'ZIPCode_92507', 'ZIPCode_92518', 'ZIPCode_92521', 'ZIPCode_92606', 'ZIPCode_92612', 'ZIPCode_92614', 'ZIPCode_92624', 'ZIPCode_92626', 'ZIPCode_92630', 'ZIPCode_92634', 'ZIPCode_92646', 'ZIPCode_92647', 'ZIPCode_92648', 'ZIPCode_92653', 'ZIPCode_92660', 'ZIPCode_92661', 'ZIPCode_92672', 'ZIPCode_92673', 'ZIPCode_92675', 'ZIPCode_92677', 'ZIPCode_92691', 'ZIPCode_92692', 'ZIPCode_92694', 'ZIPCode_92697', 'ZIPCode_92703', 'ZIPCode_92704', 'ZIPCode_92705', 'ZIPCode_92709', 'ZIPCode_92717', 'ZIPCode_92735', 'ZIPCode_92780', 'ZIPCode_92806', 'ZIPCode_92807', 'ZIPCode_92821', 'ZIPCode_92831', 'ZIPCode_92833', 'ZIPCode_92834', 'ZIPCode_92835', 'ZIPCode_92843', 'ZIPCode_92866', 'ZIPCode_92867', 'ZIPCode_92868', 'ZIPCode_92870', 'ZIPCode_92886', 'ZIPCode_93003', 'ZIPCode_93009', 'ZIPCode_93010', 'ZIPCode_93014', 'ZIPCode_93022', 'ZIPCode_93023', 'ZIPCode_93033', 'ZIPCode_93063', 'ZIPCode_93065', 'ZIPCode_93077', 'ZIPCode_93101', 'ZIPCode_93105', 'ZIPCode_93106', 'ZIPCode_93107', 'ZIPCode_93108', 'ZIPCode_93109', 'ZIPCode_93111', 'ZIPCode_93117', 'ZIPCode_93118', 'ZIPCode_93302', 'ZIPCode_93305', 'ZIPCode_93311', 'ZIPCode_93401', 'ZIPCode_93403', 'ZIPCode_93407', 'ZIPCode_93437', 'ZIPCode_93460', 'ZIPCode_93524', 'ZIPCode_93555', 'ZIPCode_93561', 'ZIPCode_93611', 'ZIPCode_93657', 'ZIPCode_93711', 'ZIPCode_93720', 'ZIPCode_93727', 'ZIPCode_93907', 'ZIPCode_93933', 'ZIPCode_93940', 'ZIPCode_93943', 'ZIPCode_93950', 'ZIPCode_93955', 'ZIPCode_94002', 'ZIPCode_94005', 'ZIPCode_94010', 'ZIPCode_94015', 'ZIPCode_94019', 'ZIPCode_94022', 'ZIPCode_94024', 'ZIPCode_94025', 'ZIPCode_94028', 'ZIPCode_94035', 'ZIPCode_94040', 'ZIPCode_94043', 'ZIPCode_94061', 'ZIPCode_94063', 'ZIPCode_94065', 'ZIPCode_94066', 'ZIPCode_94080', 'ZIPCode_94085', 'ZIPCode_94086', 'ZIPCode_94087', 'ZIPCode_94102', 'ZIPCode_94104', 'ZIPCode_94105', 'ZIPCode_94107', 'ZIPCode_94108', 'ZIPCode_94109', 'ZIPCode_94110', 'ZIPCode_94111', 'ZIPCode_94112', 'ZIPCode_94114', 'ZIPCode_94115', 'ZIPCode_94116', 'ZIPCode_94117', 'ZIPCode_94118', 'ZIPCode_94122', 'ZIPCode_94123', 'ZIPCode_94124', 'ZIPCode_94126', 'ZIPCode_94131', 'ZIPCode_94132', 'ZIPCode_94143', 'ZIPCode_94234', 'ZIPCode_94301', 'ZIPCode_94302', 'ZIPCode_94303', 'ZIPCode_94304', 'ZIPCode_94305', 'ZIPCode_94306', 'ZIPCode_94309', 'ZIPCode_94402', 'ZIPCode_94404', 'ZIPCode_94501', 'ZIPCode_94507', 'ZIPCode_94509', 'ZIPCode_94521', 'ZIPCode_94523', 'ZIPCode_94526', 'ZIPCode_94534', 'ZIPCode_94536', 'ZIPCode_94538', 'ZIPCode_94539', 'ZIPCode_94542', 'ZIPCode_94545', 'ZIPCode_94546', 'ZIPCode_94550', 'ZIPCode_94551', 'ZIPCode_94553', 'ZIPCode_94555', 'ZIPCode_94558', 'ZIPCode_94566', 'ZIPCode_94571', 'ZIPCode_94575', 'ZIPCode_94577', 'ZIPCode_94583', 'ZIPCode_94588', 'ZIPCode_94590', 'ZIPCode_94591', 'ZIPCode_94596', 'ZIPCode_94598', 'ZIPCode_94604', 'ZIPCode_94606', 'ZIPCode_94607', 'ZIPCode_94608', 'ZIPCode_94609', 'ZIPCode_94610', 'ZIPCode_94611', 'ZIPCode_94612', 'ZIPCode_94618', 'ZIPCode_94701', 'ZIPCode_94703', 'ZIPCode_94704', 'ZIPCode_94705', 'ZIPCode_94706', 'ZIPCode_94707', 'ZIPCode_94708', 'ZIPCode_94709', 'ZIPCode_94710', 'ZIPCode_94720', 'ZIPCode_94801', 'ZIPCode_94803', 'ZIPCode_94806', 'ZIPCode_94901', 'ZIPCode_94904', 'ZIPCode_94920', 'ZIPCode_94923', 'ZIPCode_94928', 'ZIPCode_94939', 'ZIPCode_94949', 'ZIPCode_94960', 'ZIPCode_94965', 'ZIPCode_94970', 'ZIPCode_94998', 'ZIPCode_95003', 'ZIPCode_95005', 'ZIPCode_95006', 'ZIPCode_95008', 'ZIPCode_95010', 'ZIPCode_95014', 'ZIPCode_95020', 'ZIPCode_95023', 'ZIPCode_95032', 'ZIPCode_95035', 'ZIPCode_95037', 'ZIPCode_95039', 'ZIPCode_95045', 'ZIPCode_95051', 'ZIPCode_95053', 'ZIPCode_95054', 'ZIPCode_95060', 'ZIPCode_95064', 'ZIPCode_95070', 'ZIPCode_95112', 'ZIPCode_95120', 'ZIPCode_95123', 'ZIPCode_95125', 'ZIPCode_95126', 'ZIPCode_95131', 'ZIPCode_95133', 'ZIPCode_95134', 'ZIPCode_95135', 'ZIPCode_95136', 'ZIPCode_95138', 'ZIPCode_95192', 'ZIPCode_95193', 'ZIPCode_95207', 'ZIPCode_95211', 'ZIPCode_95307', 'ZIPCode_95348', 'ZIPCode_95351', 'ZIPCode_95354', 'ZIPCode_95370', 'ZIPCode_95403', 'ZIPCode_95405', 'ZIPCode_95422', 'ZIPCode_95449', 'ZIPCode_95482', 'ZIPCode_95503', 'ZIPCode_95518', 'ZIPCode_95521', 'ZIPCode_95605', 'ZIPCode_95616', 'ZIPCode_95617', 'ZIPCode_95621', 'ZIPCode_95630', 'ZIPCode_95670', 'ZIPCode_95678', 'ZIPCode_95741', 'ZIPCode_95747', 'ZIPCode_95758', 'ZIPCode_95762', 'ZIPCode_95812', 'ZIPCode_95814', 'ZIPCode_95816', 'ZIPCode_95817', 'ZIPCode_95818', 'ZIPCode_95819', 'ZIPCode_95820', 'ZIPCode_95821', 'ZIPCode_95822', 'ZIPCode_95825', 'ZIPCode_95827', 'ZIPCode_95828', 'ZIPCode_95831', 'ZIPCode_95833', 'ZIPCode_95841', 'ZIPCode_95842', 'ZIPCode_95929', 'ZIPCode_95973', 'ZIPCode_96001', 'ZIPCode_96003', 'ZIPCode_96008', 'ZIPCode_96064', 'ZIPCode_96091', 'ZIPCode_96094', 'ZIPCode_96145', 'ZIPCode_96150', 'ZIPCode_96651']


plt.figure(figsize=(20,30))
tree.plot_tree(dTree,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


# Text report showing the rules of a decision tree -

print(tree.export_text(dTree,feature_names=feature_names,show_weights=True))

|--- Income <= 116.50
|   |--- Income <= 92.50
|   |   |--- ZIPCode_90601 <= 0.50
|   |   |   |--- Income <= 81.50
|   |   |   |   |--- ZIPCode_91203 <= 0.50
|   |   |   |   |   |--- ZIPCode_94305 <= 0.50
|   |   |   |   |   |   |--- weights: [2198.00, 0.00] class: 0
|   |   |   |   |   |--- ZIPCode_94305 >  0.50
|   |   |   |   |   |   |--- CCAvg_Inc% <= 52.50
|   |   |   |   |   |   |   |--- weights: [45.00, 0.00] class: 0
|   |   |   |   |   |   |--- CCAvg_Inc% >  52.50
|   |   |   |   |   |   |   |--- CCAvg_Inc% <= 53.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- CCAvg_Inc% >  53.50
|   |   |   |   |   |   |   |   |--- Income <= 71.00
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- Income >  71.00
|   |   |   |   |   |   |   |   |   |--- Age <= 43.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- Age >  43.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |--- ZIPCode_91203 >  0.50
|   |   |   |   |   |--- CreditCard <= 0.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- CreditCard >  0.50
|   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |--- Income >  81.50
|   |   |   |   |--- CCAvg_Inc% <= 43.50
|   |   |   |   |   |--- ZIPCode_92121 <= 0.50
|   |   |   |   |   |   |--- weights: [248.00, 0.00] class: 0
|   |   |   |   |   |--- ZIPCode_92121 >  0.50
|   |   |   |   |   |   |--- CreditCard <= 0.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- CreditCard >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- CCAvg_Inc% >  43.50
|   |   |   |   |   |--- CD_Account <= 0.50
|   |   |   |   |   |   |--- Income <= 83.50
|   |   |   |   |   |   |   |--- Age <= 45.50
|   |   |   |   |   |   |   |   |--- Age <= 28.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- Age >  28.00
|   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- Age >  45.50
|   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% <= 5.00
|   |   |   |   |   |   |   |   |   |--- ZIPCode_94720 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- ZIPCode_94720 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% >  5.00
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- Income >  83.50
|   |   |   |   |   |   |   |--- ZIPCode_94122 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [30.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- ZIPCode_94122 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- CD_Account >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |--- ZIPCode_90601 >  0.50
|   |   |   |--- Income <= 71.00
|   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |--- Income >  71.00
|   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |--- Income >  92.50
|   |   |--- CCAvg_Inc% <= 37.50
|   |   |   |--- ZIPCode_93106 <= 0.50
|   |   |   |   |--- ZIPCode_90049 <= 0.50
|   |   |   |   |   |--- ZIPCode_95822 <= 0.50
|   |   |   |   |   |   |--- ZIPCode_94705 <= 0.50
|   |   |   |   |   |   |   |--- ZIPCode_91129 <= 0.50
|   |   |   |   |   |   |   |   |--- ZIPCode_92007 <= 0.50
|   |   |   |   |   |   |   |   |   |--- ZIPCode_94110 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- ZIPCode_94305 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- ZIPCode_94305 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- ZIPCode_94110 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- Age <= 48.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- Age >  48.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- ZIPCode_92007 >  0.50
|   |   |   |   |   |   |   |   |   |--- Education <= 2.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- Education >  2.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- ZIPCode_91129 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- ZIPCode_94705 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- ZIPCode_95822 >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- ZIPCode_90049 >  0.50
|   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |--- ZIPCode_93106 >  0.50
|   |   |   |   |--- Experience <= 33.50
|   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |--- Experience >  33.50
|   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |--- CCAvg_Inc% >  37.50
|   |   |   |--- Education <= 1.50
|   |   |   |   |--- CD_Account <= 0.50
|   |   |   |   |   |--- Experience <= 5.00
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- Experience >  5.00
|   |   |   |   |   |   |--- ZIPCode_92709 <= 0.50
|   |   |   |   |   |   |   |--- ZIPCode_90034 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [24.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- ZIPCode_90034 >  0.50
|   |   |   |   |   |   |   |   |--- Age <= 50.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- Age >  50.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- ZIPCode_92709 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- CD_Account >  0.50
|   |   |   |   |   |--- CCAvg_Inc% <= 59.00
|   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |--- CCAvg_Inc% >  59.00
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |--- Education >  1.50
|   |   |   |   |--- Income <= 106.00
|   |   |   |   |   |--- Family <= 2.50
|   |   |   |   |   |   |--- CCAvg_Inc% <= 51.50
|   |   |   |   |   |   |   |--- CD_Account <= 0.50
|   |   |   |   |   |   |   |   |--- ZIPCode_90277 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |--- ZIPCode_90277 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- CD_Account >  0.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- CCAvg_Inc% >  51.50
|   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |--- Family >  2.50
|   |   |   |   |   |   |--- Age <= 62.50
|   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |--- Age >  62.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |--- Income >  106.00
|   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|--- Income >  116.50
|   |--- Education <= 1.50
|   |   |--- Family <= 2.50
|   |   |   |--- weights: [375.00, 0.00] class: 0
|   |   |--- Family >  2.50
|   |   |   |--- weights: [0.00, 47.00] class: 1
|   |--- Education >  1.50
|   |   |--- weights: [0.00, 222.00] class: 1


# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(dTree.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                Imp
Education     0.403
Income        0.310
Family        0.143
CCAvg_Inc%    0.049
Age           0.020
...             ...
ZIPCode_92110 0.000
ZIPCode_92109 0.000
ZIPCode_92106 0.000
ZIPCode_92104 0.000
ZIPCode_96651 0.000

[478 rows x 1 columns]


dTree1 = DecisionTreeClassifier(criterion = 'gini',max_depth=3,random_state=1)
dTree1.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)


make_confusion_matrix(dTree1, y_test)


# Accuracy on train and test
print("Accuracy on training set : ",dTree1.score(X_train, y_train))
print("Accuracy on test set : ",dTree1.score(X_test, y_test))
# Recall on train and test
get_recall_score(dTree1)

Accuracy on training set :  0.9822857142857143
Accuracy on test set :  0.9753333333333334
Recall on training set :  0.8126888217522659
Recall on test set :  0.7516778523489933


plt.figure(figsize=(15,10))

tree.plot_tree(dTree1,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


# Text report showing the rules of a decision tree -

print(tree.export_text(dTree1,feature_names=feature_names,show_weights=True))

|--- Income <= 116.50
|   |--- Income <= 92.50
|   |   |--- ZIPCode_90601 <= 0.50
|   |   |   |--- weights: [2550.00, 14.00] class: 0
|   |   |--- ZIPCode_90601 >  0.50
|   |   |   |--- weights: [2.00, 1.00] class: 0
|   |--- Income >  92.50
|   |   |--- CCAvg_Inc% <= 37.50
|   |   |   |--- weights: [207.00, 14.00] class: 0
|   |   |--- CCAvg_Inc% >  37.50
|   |   |   |--- weights: [35.00, 33.00] class: 0
|--- Income >  116.50
|   |--- Education <= 1.50
|   |   |--- Family <= 2.50
|   |   |   |--- weights: [375.00, 0.00] class: 0
|   |   |--- Family >  2.50
|   |   |   |--- weights: [0.00, 47.00] class: 1
|   |--- Education >  1.50
|   |   |--- weights: [0.00, 222.00] class: 1


# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(dTree1.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                Imp
Education     0.450
Income        0.348
Family        0.164
CCAvg_Inc%    0.036
ZIPCode_90601 0.001
...             ...
ZIPCode_92093 0.000
ZIPCode_92084 0.000
ZIPCode_92069 0.000
ZIPCode_92068 0.000
ZIPCode_96651 0.000

[478 rows x 1 columns]


from sklearn.model_selection import GridSearchCV


# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from
## add from article
parameters = {'max_depth': np.arange(1,10),
              'min_samples_leaf': [1, 2, 5, 7, 10,15,20],
              'max_leaf_nodes' : [2, 3, 5, 10],
              'min_impurity_decrease': [0.001,0.01,0.1]
             }

# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(metrics.recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=4, max_leaf_nodes=10,
                       min_impurity_decrease=0.001, min_samples_leaf=7,
                       random_state=1)

DecisionTreeClassifier(max_depth=4, max_leaf_nodes=10,
                       min_impurity_decrease=0.001, min_samples_leaf=7,
                       random_state=1)


make_confusion_matrix(estimator,y_test)


# Accuracy on train and test
print("Accuracy on training set : ",estimator.score(X_train, y_train))
print("Accuracy on test set : ",estimator.score(X_test, y_test))
# Recall on train and test
get_recall_score(estimator)

Accuracy on training set :  0.9871428571428571
Accuracy on test set :  0.9786666666666667
Recall on training set :  0.8912386706948641
Recall on test set :  0.8187919463087249


plt.figure(figsize=(15,10))

tree.plot_tree(estimator,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


# Text report showing the rules of a decision tree -

print(tree.export_text(estimator,feature_names=feature_names,show_weights=True))

|--- Income <= 116.50
|   |--- Income <= 92.50
|   |   |--- weights: [2552.00, 15.00] class: 0
|   |--- Income >  92.50
|   |   |--- CCAvg_Inc% <= 37.50
|   |   |   |--- weights: [207.00, 14.00] class: 0
|   |   |--- CCAvg_Inc% >  37.50
|   |   |   |--- Education <= 1.50
|   |   |   |   |--- weights: [26.00, 7.00] class: 0
|   |   |   |--- Education >  1.50
|   |   |   |   |--- weights: [9.00, 26.00] class: 1
|--- Income >  116.50
|   |--- Education <= 1.50
|   |   |--- Family <= 2.50
|   |   |   |--- weights: [375.00, 0.00] class: 0
|   |   |--- Family >  2.50
|   |   |   |--- weights: [0.00, 47.00] class: 1
|   |--- Education >  1.50
|   |   |--- weights: [0.00, 222.00] class: 1


# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(estimator.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

#Here we will see that importance of features has increased

                Imp
Education     0.461
Income        0.342
Family        0.161
CCAvg_Inc%    0.036
ZIPCode_94234 0.000
...             ...
ZIPCode_92084 0.000
ZIPCode_92069 0.000
ZIPCode_92068 0.000
ZIPCode_92064 0.000
ZIPCode_96651 0.000

[478 rows x 1 columns]


clf = DecisionTreeClassifier(random_state=1)
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities


pd.DataFrame(path)


fig, ax = plt.subplots(figsize=(10,5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()


clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
    clf.fit(X_train, y_train)
    clfs.append(clf)
print("Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
      clfs[-1].tree_.node_count, ccp_alphas[-1]))

Number of nodes in the last tree is: 1 with ccp_alpha: 0.056364969335601575


clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1,figsize=(10,7))
ax[0].plot(ccp_alphas, node_counts, marker='o', drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker='o', drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()


train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]


fig, ax = plt.subplots(figsize=(10,5))
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_scores, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, test_scores, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()


index_best_model = np.argmax(test_scores)
best_model = clfs[index_best_model]
print(best_model)
print('Training accuracy of best model: ',best_model.score(X_train, y_train))
print('Test accuracy of best model: ',best_model.score(X_test, y_test))

DecisionTreeClassifier(ccp_alpha=0.0005952380952380953, random_state=1)
Training accuracy of best model:  0.9908571428571429
Test accuracy of best model:  0.9786666666666667


recall_train=[]
for clf in clfs:
    pred_train3=clf.predict(X_train)
    values_train=metrics.recall_score(y_train,pred_train3)
    recall_train.append(values_train)


recall_test=[]
for clf in clfs:
    pred_test3=clf.predict(X_test)
    values_test=metrics.recall_score(y_test,pred_test3)
    recall_test.append(values_test)


fig, ax = plt.subplots(figsize=(15,5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()


# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=0.0004761904761904762, random_state=1)


make_confusion_matrix(best_model,y_test)


# Recall on train and test
get_recall_score(best_model)

Recall on training set :  0.9577039274924471
Recall on test set :  0.8389261744966443


plt.figure(figsize=(17,15))

tree.plot_tree(best_model,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


# Text report showing the rules of a decision tree -

print(tree.export_text(best_model,feature_names=feature_names,show_weights=True))

|--- Income <= 116.50
|   |--- Income <= 92.50
|   |   |--- ZIPCode_90601 <= 0.50
|   |   |   |--- Income <= 81.50
|   |   |   |   |--- weights: [2258.00, 3.00] class: 0
|   |   |   |--- Income >  81.50
|   |   |   |   |--- CCAvg_Inc% <= 43.50
|   |   |   |   |   |--- weights: [250.00, 1.00] class: 0
|   |   |   |   |--- CCAvg_Inc% >  43.50
|   |   |   |   |   |--- CD_Account <= 0.50
|   |   |   |   |   |   |--- Income <= 83.50
|   |   |   |   |   |   |   |--- Age <= 45.50
|   |   |   |   |   |   |   |   |--- Age <= 28.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- Age >  28.00
|   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- Age >  45.50
|   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% <= 5.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |--- year_Mortgage_Inc% >  5.00
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- Income >  83.50
|   |   |   |   |   |   |   |--- ZIPCode_94122 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [30.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- ZIPCode_94122 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- CD_Account >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |--- ZIPCode_90601 >  0.50
|   |   |   |--- weights: [2.00, 1.00] class: 0
|   |--- Income >  92.50
|   |   |--- CCAvg_Inc% <= 37.50
|   |   |   |--- ZIPCode_93106 <= 0.50
|   |   |   |   |--- ZIPCode_90049 <= 0.50
|   |   |   |   |   |--- ZIPCode_95822 <= 0.50
|   |   |   |   |   |   |--- ZIPCode_94705 <= 0.50
|   |   |   |   |   |   |   |--- ZIPCode_91129 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [206.00, 8.00] class: 0
|   |   |   |   |   |   |   |--- ZIPCode_91129 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- ZIPCode_94705 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- ZIPCode_95822 >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- ZIPCode_90049 >  0.50
|   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |--- ZIPCode_93106 >  0.50
|   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |--- CCAvg_Inc% >  37.50
|   |   |   |--- Education <= 1.50
|   |   |   |   |--- CD_Account <= 0.50
|   |   |   |   |   |--- Experience <= 5.00
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- Experience >  5.00
|   |   |   |   |   |   |--- ZIPCode_92709 <= 0.50
|   |   |   |   |   |   |   |--- weights: [25.00, 1.00] class: 0
|   |   |   |   |   |   |--- ZIPCode_92709 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- CD_Account >  0.50
|   |   |   |   |   |--- weights: [1.00, 4.00] class: 1
|   |   |   |--- Education >  1.50
|   |   |   |   |--- Income <= 106.00
|   |   |   |   |   |--- Family <= 2.50
|   |   |   |   |   |   |--- CCAvg_Inc% <= 51.50
|   |   |   |   |   |   |   |--- CD_Account <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 5.00] class: 1
|   |   |   |   |   |   |   |--- CD_Account >  0.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- CCAvg_Inc% >  51.50
|   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |--- Family >  2.50
|   |   |   |   |   |   |--- Age <= 62.50
|   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |--- Age >  62.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |--- Income >  106.00
|   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|--- Income >  116.50
|   |--- Education <= 1.50
|   |   |--- Family <= 2.50
|   |   |   |--- weights: [375.00, 0.00] class: 0
|   |   |--- Family >  2.50
|   |   |   |--- weights: [0.00, 47.00] class: 1
|   |--- Education >  1.50
|   |   |--- weights: [0.00, 222.00] class: 1


# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(best_model.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                Imp
Education     0.422
Income        0.322
Family        0.152
CCAvg_Inc%    0.042
CD_Account    0.018
...             ...
ZIPCode_92096 0.000
ZIPCode_92093 0.000
ZIPCode_92084 0.000
ZIPCode_92069 0.000
ZIPCode_96651 0.000

[478 rows x 1 columns]


comparison_frame = pd.DataFrame({'Model':['Initial decision tree model','Decision tree with restricted maximum depth','Decision treee with hyperparameter tuning',
                                         'Decision tree with post-pruning'], 'Train_Recall':[1,0.81,0.89,0.95], 'Test_Recall':[0.83,0.75,0.81,0.84]})
comparison_frame

	count	mean	std	min	25%	50%	75%	max
ID	5000.000	2500.500	1443.520	1.000	1250.750	2500.500	3750.250	5000.000
Age	5000.000	45.338	11.463	23.000	35.000	45.000	55.000	67.000
Experience	5000.000	20.105	11.468	-3.000	10.000	20.000	30.000	43.000
Income	5000.000	73.774	46.034	8.000	39.000	64.000	98.000	224.000
ZIPCode	5000.000	93169.257	1759.455	90005.000	91911.000	93437.000	94608.000	96651.000
Family	5000.000	2.396	1.148	1.000	1.000	2.000	3.000	4.000
CCAvg	5000.000	1.938	1.748	0.000	0.700	1.500	2.500	10.000
Education	5000.000	1.881	0.840	1.000	1.000	2.000	3.000	3.000
Mortgage	5000.000	56.499	101.714	0.000	0.000	0.000	101.000	635.000
Personal_Loan	5000.000	0.096	0.295	0.000	0.000	0.000	0.000	1.000
Securities_Account	5000.000	0.104	0.306	0.000	0.000	0.000	0.000	1.000
CD_Account	5000.000	0.060	0.238	0.000	0.000	0.000	0.000	1.000
Online	5000.000	0.597	0.491	0.000	0.000	1.000	1.000	1.000
CreditCard	5000.000	0.294	0.456	0.000	0.000	0.000	1.000	1.000

	Logistic Regression sklearn	Logistic Regression-0.15 Threshold	Logistic Regression-0.37 Threshold
Accuracy	0.961	0.930	0.959
Recall	0.695	0.903	0.764
Precision	0.868	0.585	0.793
F1	0.772	0.710	0.778

	Logistic Regression sklearn	Logistic Regression-0.15 Threshold	Logistic Regression-0.37 Threshold
Accuracy	0.945	0.916	0.943
Recall	0.564	0.819	0.624
Precision	0.832	0.552	0.756
F1	0.672	0.659	0.684

	ID	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Securities_Account	CreditCard
0	1	25	1	49	91107	4	1.600	1	1	0
1	2	45	19	34	90089	3	1.500	1	1	0
2	3	39	15	11	94720	1	1.000	1	0	0
3	4	35	9	100	94112	1	2.700	2	0	0
4	5	35	8	45	91330	4	1.000	2	0	1

	ID	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Online	CreditCard
4995	4996	29	3	40	92697	1	1.900	3	0	1	0
4996	4997	30	4	15	92037	4	0.400	1	85	1	0
4997	4998	63	39	24	93023	2	0.300	3	0	0	0
4998	4999	65	40	49	90034	3	0.500	2	0	1	0
4999	5000	28	4	83	92612	3	0.800	1	0	1	1

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Securities_Account	Online	CreditCard
89	25	-1	113	94303	4	2.300	3	0	0	0	1
226	24	-1	39	94085	2	1.700	2	0	0	0	0
315	24	-2	51	90630	3	0.300	3	0	0	1	0
451	28	-2	48	94132	2	1.750	3	89	0	1	0
524	24	-1	75	93014	4	0.200	1	0	0	1	0
536	25	-1	43	92173	3	2.400	2	176	0	1	0
540	25	-1	109	94010	4	2.300	3	314	0	1	0
576	25	-1	48	92870	3	0.300	3	0	0	0	1
583	24	-1	38	95045	2	1.700	2	0	0	1	0
597	24	-2	125	92835	2	7.200	1	0	1	0	1
649	25	-1	82	92677	4	2.100	3	0	0	1	0
670	23	-1	61	92374	4	2.600	1	239	0	1	0
686	24	-1	38	92612	4	0.600	2	0	0	1	0
793	24	-2	150	94720	2	2.000	1	0	0	1	0
889	24	-2	82	91103	2	1.600	3	0	0	1	1
909	23	-1	149	91709	1	6.330	1	305	0	0	1
1173	24	-1	35	94305	2	1.700	2	0	0	0	0
1428	25	-1	21	94583	4	0.400	1	90	0	1	0
1522	25	-1	101	94720	4	2.300	3	256	0	0	1
1905	25	-1	112	92507	2	2.000	1	241	0	1	0
2102	25	-1	81	92647	2	1.600	3	0	0	1	1
2430	23	-1	73	92120	4	2.600	1	0	0	1	0
2466	24	-2	80	94105	2	1.600	3	0	0	1	0
2545	25	-1	39	94720	3	2.400	2	0	0	1	0
2618	23	-3	55	92704	3	2.400	2	145	0	1	0
2717	23	-2	45	95422	4	0.600	2	0	0	1	1
2848	24	-1	78	94720	2	1.800	2	0	0	0	0
2876	24	-2	80	91107	2	1.600	3	238	0	0	0
2962	23	-2	81	91711	2	1.800	2	0	0	0	0
2980	25	-1	53	94305	3	2.400	2	0	0	0	0
3076	29	-1	62	92672	2	1.750	3	0	0	0	1
3130	23	-2	82	92152	2	1.800	2	0	1	0	1
3157	23	-1	13	94720	4	1.000	1	84	0	1	0
3279	26	-1	44	94901	1	2.000	2	0	0	0	0
3284	25	-1	101	95819	4	2.100	3	0	0	0	1
3292	25	-1	13	95616	4	0.400	1	0	1	0	0
3394	25	-1	113	90089	4	2.100	3	0	0	1	0
3425	23	-1	12	91605	4	1.000	1	90	0	1	0
3626	24	-3	28	90089	4	1.000	3	0	0	0	0
3796	24	-2	50	94920	3	2.400	2	0	1	0	0
3824	23	-1	12	95064	4	1.000	1	0	1	0	1
3887	24	-2	118	92634	2	7.200	1	0	1	1	0
3946	25	-1	40	93117	3	2.400	2	0	0	1	0
4015	25	-1	139	93106	2	2.000	1	0	0	0	1
4088	29	-1	71	94801	2	1.750	3	0	0	0	0
4116	24	-2	135	90065	2	7.200	1	0	0	1	0
4285	23	-3	149	93555	2	7.200	1	0	0	1	0
4411	23	-2	75	90291	2	1.800	2	0	0	1	1
4481	25	-2	35	95045	4	1.000	3	0	0	1	0
4514	24	-3	41	91768	4	1.000	3	0	0	1	0
4582	25	-1	69	92691	3	0.300	3	0	0	1	0
4957	29	-1	50	95842	2	1.750	3	0	0	0	1

	Age	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard
105	24	35	94704	3	0.100	2	0	0	1	0	1	0
151	26	132	92834	3	6.500	3	0	1	0	0	0	1
155	24	60	94596	4	1.600	1	0	0	0	0	1	0
156	26	15	92131	4	0.400	1	0	0	0	0	0	1
160	29	134	95819	4	6.500	3	0	1	0	0	0	0
182	24	135	95133	1	1.500	1	0	0	0	0	1	0
256	26	99	92697	4	2.300	3	0	0	0	0	0	1
347	25	43	94305	2	1.600	3	0	0	1	1	1	1
363	25	30	92691	2	1.700	2	0	0	0	0	0	0
379	25	28	92093	2	1.700	2	0	0	0	0	0	0
418	27	33	90089	4	1.000	3	0	0	0	0	0	0
466	25	13	91342	2	0.900	3	0	0	0	0	1	0
495	25	44	94545	4	0.600	2	0	0	0	0	1	1
873	24	88	90740	3	0.800	1	134	0	0	0	0	0
1057	30	63	95503	2	1.750	3	0	0	0	0	1	0
1181	25	65	90095	4	0.200	1	0	0	1	0	0	0
1337	26	179	92028	4	2.100	2	0	1	0	0	0	0
1732	25	88	94566	2	1.800	2	319	0	0	0	1	1
1765	26	149	95051	2	7.200	1	154	0	0	0	0	0
1847	25	52	95126	3	2.600	3	159	0	0	0	0	0
2009	25	99	92735	1	1.900	1	323	0	0	0	0	0
2157	25	71	93727	4	0.200	1	78	0	1	0	0	0
2165	27	38	95929	4	1.000	3	154	0	0	0	1	0
2241	26	14	94301	4	0.400	1	94	0	0	0	1	0
2259	24	82	90401	3	0.800	1	0	0	0	0	1	0
2417	25	53	90095	2	1.600	3	0	0	0	0	1	1
2648	26	155	93105	2	7.200	1	0	0	0	0	0	0
2652	24	44	90089	4	1.600	1	180	0	0	0	1	0
2756	27	40	91301	4	1.000	3	0	0	0	0	1	0
3075	26	85	95616	2	1.600	3	0	0	0	0	0	0
3084	26	129	90028	3	0.700	2	0	1	0	0	0	0
3135	25	91	95039	2	1.800	2	321	0	0	0	0	0
3147	26	30	94024	4	1.300	3	0	0	0	0	1	1
3378	25	44	94536	4	0.600	2	0	0	0	0	0	1
3538	26	23	93561	1	0.100	2	0	0	0	0	0	0
3747	26	83	91360	3	3.900	2	0	1	0	0	1	0
3765	26	54	94706	3	0.300	3	0	0	0	0	1	0
3818	26	102	94305	4	2.300	3	0	0	0	0	0	0
3870	25	25	94596	2	0.900	3	0	0	0	0	0	0
3889	26	19	93014	1	0.100	2	121	0	0	0	1	0
3908	24	44	90638	3	0.100	2	0	0	0	0	0	0
3982	24	119	94566	1	1.500	1	0	0	0	0	1	0
4017	26	42	92009	4	1.300	3	153	0	0	0	1	0
4046	25	72	94303	3	2.600	3	0	0	0	0	1	0
4077	26	71	92093	4	1.800	2	0	0	1	0	1	0
4080	27	40	90068	1	2.000	2	110	0	0	0	0	1
4109	27	30	93107	4	1.000	3	0	0	0	0	1	1
4282	26	195	92093	3	6.330	3	0	1	1	1	1	0
4321	27	34	92717	1	2.000	2	112	0	0	0	0	1
4393	24	59	95521	4	1.600	1	0	0	0	0	0	0
4425	26	164	95973	2	4.000	3	301	1	0	0	1	0
4529	27	40	92103	4	1.000	3	0	0	0	0	0	0
4551	27	28	91330	4	1.500	2	0	0	0	0	1	0
4566	24	131	92831	1	5.400	1	0	0	0	0	1	0
4568	26	44	94305	4	1.300	3	0	0	1	0	0	0
4584	26	49	90089	3	2.400	2	0	0	0	0	0	0
4677	25	38	93407	2	1.600	3	0	0	0	0	0	0
4679	26	161	94551	2	7.200	1	0	0	0	0	0	0
4712	25	14	94309	2	0.900	3	0	0	0	0	0	1
4782	26	150	91311	2	7.200	1	0	0	0	0	0	1
4796	26	42	95032	4	1.300	3	0	0	1	0	0	0
4874	26	75	94061	3	0.300	3	0	0	0	0	0	0
4901	26	54	96094	3	1.100	2	0	0	0	0	1	0
4934	26	85	93950	2	1.600	3	0	0	0	0	1	1
4943	26	12	96003	1	0.100	2	0	0	0	0	1	0
4989	24	38	93555	1	1.000	3	0	0	0	0	1	0

	Age	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Securities_Account	Online	CreditCard	Income_bin
89	25	113	94303	4	2.300	3	0	0	0	1	[100..125)
226	24	39	94085	2	1.700	2	0	0	0	0	[25..50)
315	24	51	90630	3	0.300	3	0	0	1	0	[50..75)
451	28	48	94132	2	1.750	3	89	0	1	0	[25..50)
524	24	75	93014	4	0.200	1	0	0	1	0	[50..75)
536	25	43	92173	3	2.400	2	176	0	1	0	[25..50)
540	25	109	94010	4	2.300	3	314	0	1	0	[100..125)
576	25	48	92870	3	0.300	3	0	0	0	1	[25..50)
583	24	38	95045	2	1.700	2	0	0	1	0	[25..50)
597	24	125	92835	2	7.200	1	0	1	0	1	[100..125)
649	25	82	92677	4	2.100	3	0	0	1	0	[75..100)
670	23	61	92374	4	2.600	1	239	0	1	0	[50..75)
686	24	38	92612	4	0.600	2	0	0	1	0	[25..50)
793	24	150	94720	2	2.000	1	0	0	1	0	[125..150)
889	24	82	91103	2	1.600	3	0	0	1	1	[75..100)
909	23	149	91709	1	6.330	1	305	0	0	1	[125..150)
1173	24	35	94305	2	1.700	2	0	0	0	0	[25..50)
1428	25	21	94583	4	0.400	1	90	0	1	0	[0..25)
1522	25	101	94720	4	2.300	3	256	0	0	1	[100..125)
1905	25	112	92507	2	2.000	1	241	0	1	0	[100..125)
2102	25	81	92647	2	1.600	3	0	0	1	1	[75..100)
2430	23	73	92120	4	2.600	1	0	0	1	0	[50..75)
2466	24	80	94105	2	1.600	3	0	0	1	0	[75..100)
2545	25	39	94720	3	2.400	2	0	0	1	0	[25..50)
2618	23	55	92704	3	2.400	2	145	0	1	0	[50..75)
2717	23	45	95422	4	0.600	2	0	0	1	1	[25..50)
2848	24	78	94720	2	1.800	2	0	0	0	0	[75..100)
2876	24	80	91107	2	1.600	3	238	0	0	0	[75..100)
2962	23	81	91711	2	1.800	2	0	0	0	0	[75..100)
2980	25	53	94305	3	2.400	2	0	0	0	0	[50..75)
3076	29	62	92672	2	1.750	3	0	0	0	1	[50..75)
3130	23	82	92152	2	1.800	2	0	1	0	1	[75..100)
3157	23	13	94720	4	1.000	1	84	0	1	0	[0..25)
3279	26	44	94901	1	2.000	2	0	0	0	0	[25..50)
3284	25	101	95819	4	2.100	3	0	0	0	1	[100..125)
3292	25	13	95616	4	0.400	1	0	1	0	0	[0..25)
3394	25	113	90089	4	2.100	3	0	0	1	0	[100..125)
3425	23	12	91605	4	1.000	1	90	0	1	0	[0..25)
3626	24	28	90089	4	1.000	3	0	0	0	0	[25..50)
3796	24	50	94920	3	2.400	2	0	1	0	0	[25..50)
3824	23	12	95064	4	1.000	1	0	1	0	1	[0..25)
3887	24	118	92634	2	7.200	1	0	1	1	0	[100..125)
3946	25	40	93117	3	2.400	2	0	0	1	0	[25..50)
4015	25	139	93106	2	2.000	1	0	0	0	1	[125..150)
4088	29	71	94801	2	1.750	3	0	0	0	0	[50..75)
4116	24	135	90065	2	7.200	1	0	0	1	0	[125..150)
4285	23	149	93555	2	7.200	1	0	0	1	0	[125..150)
4411	23	75	90291	2	1.800	2	0	0	1	1	[50..75)
4481	25	35	95045	4	1.000	3	0	0	1	0	[25..50)
4514	24	41	91768	4	1.000	3	0	0	1	0	[25..50)
4582	25	69	92691	3	0.300	3	0	0	1	0	[50..75)
4957	29	50	95842	2	1.750	3	0	0	0	1	[25..50)

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Securities_Account	Online	CreditCard	modeX	medianX	allPosX
89	25	-1	113	94303	4	2.300	3	0	0	0	1	-33	-21.000	1
226	24	-1	39	94085	2	1.700	2	0	0	0	0	-33	-21.000	1
315	24	-2	51	90630	3	0.300	3	0	0	1	0	-34	-22.000	2
451	28	-2	48	94132	2	1.750	3	89	0	1	0	-34	-22.000	2
524	24	-1	75	93014	4	0.200	1	0	0	1	0	-33	-21.000	1
536	25	-1	43	92173	3	2.400	2	176	0	1	0	-33	-21.000	1
540	25	-1	109	94010	4	2.300	3	314	0	1	0	-33	-21.000	1
576	25	-1	48	92870	3	0.300	3	0	0	0	1	-33	-21.000	1
583	24	-1	38	95045	2	1.700	2	0	0	1	0	-33	-21.000	1
597	24	-2	125	92835	2	7.200	1	0	1	0	1	-34	-22.000	2
649	25	-1	82	92677	4	2.100	3	0	0	1	0	-33	-21.000	1
670	23	-1	61	92374	4	2.600	1	239	0	1	0	-33	-21.000	1
686	24	-1	38	92612	4	0.600	2	0	0	1	0	-33	-21.000	1
793	24	-2	150	94720	2	2.000	1	0	0	1	0	-34	-22.000	2
889	24	-2	82	91103	2	1.600	3	0	0	1	1	-34	-22.000	2
909	23	-1	149	91709	1	6.330	1	305	0	0	1	-33	-21.000	1
1173	24	-1	35	94305	2	1.700	2	0	0	0	0	-33	-21.000	1
1428	25	-1	21	94583	4	0.400	1	90	0	1	0	-33	-21.000	1
1522	25	-1	101	94720	4	2.300	3	256	0	0	1	-33	-21.000	1
1905	25	-1	112	92507	2	2.000	1	241	0	1	0	-33	-21.000	1
2102	25	-1	81	92647	2	1.600	3	0	0	1	1	-33	-21.000	1
2430	23	-1	73	92120	4	2.600	1	0	0	1	0	-33	-21.000	1
2466	24	-2	80	94105	2	1.600	3	0	0	1	0	-34	-22.000	2
2545	25	-1	39	94720	3	2.400	2	0	0	1	0	-33	-21.000	1
2618	23	-3	55	92704	3	2.400	2	145	0	1	0	-35	-23.000	3
2717	23	-2	45	95422	4	0.600	2	0	0	1	1	-34	-22.000	2
2848	24	-1	78	94720	2	1.800	2	0	0	0	0	-33	-21.000	1
2876	24	-2	80	91107	2	1.600	3	238	0	0	0	-34	-22.000	2
2962	23	-2	81	91711	2	1.800	2	0	0	0	0	-34	-22.000	2
2980	25	-1	53	94305	3	2.400	2	0	0	0	0	-33	-21.000	1
3076	29	-1	62	92672	2	1.750	3	0	0	0	1	-33	-21.000	1
3130	23	-2	82	92152	2	1.800	2	0	1	0	1	-34	-22.000	2
3157	23	-1	13	94720	4	1.000	1	84	0	1	0	-33	-21.000	1
3279	26	-1	44	94901	1	2.000	2	0	0	0	0	-33	-21.000	1
3284	25	-1	101	95819	4	2.100	3	0	0	0	1	-33	-21.000	1
3292	25	-1	13	95616	4	0.400	1	0	1	0	0	-33	-21.000	1
3394	25	-1	113	90089	4	2.100	3	0	0	1	0	-33	-21.000	1
3425	23	-1	12	91605	4	1.000	1	90	0	1	0	-33	-21.000	1
3626	24	-3	28	90089	4	1.000	3	0	0	0	0	-35	-23.000	3
3796	24	-2	50	94920	3	2.400	2	0	1	0	0	-34	-22.000	2
3824	23	-1	12	95064	4	1.000	1	0	1	0	1	-33	-21.000	1
3887	24	-2	118	92634	2	7.200	1	0	1	1	0	-34	-22.000	2
3946	25	-1	40	93117	3	2.400	2	0	0	1	0	-33	-21.000	1
4015	25	-1	139	93106	2	2.000	1	0	0	0	1	-33	-21.000	1
4088	29	-1	71	94801	2	1.750	3	0	0	0	0	-33	-21.000	1
4116	24	-2	135	90065	2	7.200	1	0	0	1	0	-34	-22.000	2
4285	23	-3	149	93555	2	7.200	1	0	0	1	0	-35	-23.000	3
4411	23	-2	75	90291	2	1.800	2	0	0	1	1	-34	-22.000	2
4481	25	-2	35	95045	4	1.000	3	0	0	1	0	-34	-22.000	2
4514	24	-3	41	91768	4	1.000	3	0	0	1	0	-35	-23.000	3
4582	25	-1	69	92691	3	0.300	3	0	0	1	0	-33	-21.000	1
4957	29	-1	50	95842	2	1.750	3	0	0	0	1	-33	-21.000	1

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	Income_bin
58	28	2	93	94065	2	0.200	1	0	0	0	0	0	0	[75..100)
85	27	2	109	94005	4	1.800	3	0	0	0	0	0	0	[100..125)
222	26	2	104	94306	3	2.500	1	0	0	0	0	0	0	[100..125)
277	29	2	30	92126	4	1.000	3	0	0	0	0	0	0	[25..50)
318	27	2	110	95670	4	1.800	3	190	0	0	0	1	0	[100..125)
349	26	2	60	93407	2	3.000	1	132	1	0	0	0	0	[50..75)
397	26	2	48	90503	3	0.700	2	0	0	0	0	1	0	[25..50)
401	29	2	30	95747	4	1.500	2	112	0	0	0	0	1	[25..50)
526	26	2	205	93106	1	6.330	1	271	0	0	0	0	1	[200..225]
533	27	2	101	92807	1	1.900	1	0	0	0	0	0	0	[100..125)
546	27	2	68	94025	3	2.600	3	203	0	1	0	0	0	[50..75)
554	28	2	149	94720	2	7.200	1	0	0	0	0	1	0	[125..150)
608	27	2	55	91910	4	1.700	2	0	0	0	0	1	0	[50..75)
675	29	2	33	91711	1	2.000	2	160	0	0	0	0	0	[25..50)
692	26	2	30	94720	1	1.000	3	111	0	0	0	0	0	[25..50)
770	26	2	172	94551	2	6.900	2	0	1	0	0	1	0	[150..175)
798	29	2	38	93063	1	2.000	2	0	0	0	0	0	0	[25..50)
853	27	2	155	95138	1	0.800	1	0	0	0	0	0	0	[150..175)
864	28	2	10	94080	1	0.100	2	0	0	0	0	1	0	[0..25)
904	28	2	51	90503	4	1.800	2	0	0	1	0	0	0	[50..75)
1059	28	2	11	91203	1	0.100	2	0	0	0	0	1	1	[0..25)
1113	28	2	70	90630	3	0.300	3	0	0	0	0	0	1	[50..75)
1182	28	2	19	94720	4	0.400	1	0	0	0	0	1	1	[0..25)
1213	27	2	78	93943	4	0.200	1	87	0	0	0	0	0	[75..100)
1238	28	2	63	91116	2	1.600	3	0	0	0	0	1	0	[50..75)
1265	32	2	71	95014	2	1.750	3	108	0	0	0	0	0	[50..75)
1275	27	2	92	95616	2	3.100	1	178	0	0	0	1	0	[75..100)
1307	26	2	195	94546	1	6.330	1	0	0	0	0	1	1	[175..200)
1349	26	2	171	93943	3	6.000	2	0	1	0	0	1	0	[150..175)
1350	29	2	29	90266	4	1.500	2	0	0	0	0	0	1	[25..50)
1432	26	2	195	90245	1	6.330	1	0	0	0	0	0	0	[175..200)
1624	28	2	31	90024	2	0.300	2	0	0	1	0	1	0	[25..50)
1722	26	2	72	92647	4	2.600	1	0	0	1	1	1	0	[50..75)
1839	28	2	43	95616	4	1.300	3	0	0	0	0	1	1	[25..50)
1895	26	2	72	95003	4	2.600	1	0	0	0	0	1	0	[50..75)
1931	28	2	140	92122	2	2.000	1	0	0	0	0	1	0	[125..150)
1958	28	2	42	95762	1	1.500	1	0	0	0	0	1	1	[25..50)
1972	28	2	114	94606	4	2.100	3	0	0	0	0	1	0	[100..125)
2000	28	2	22	95670	1	0.100	2	0	0	0	0	1	0	[0..25)
2079	26	2	40	94132	1	1.000	3	0	0	0	0	1	0	[25..50)
2112	27	2	103	93117	1	1.900	1	120	0	0	0	1	0	[100..125)
2123	28	2	9	95014	1	0.100	2	0	0	0	0	1	0	[0..25)
2186	26	2	92	96001	2	0.200	1	0	0	0	0	1	0	[75..100)
2270	26	2	51	92103	4	2.600	1	0	0	0	0	1	0	[50..75)
2304	27	2	170	95818	3	4.700	1	0	1	0	0	1	0	[150..175)
2314	27	2	112	94501	4	1.800	3	0	0	1	0	1	0	[100..125)
2328	27	2	130	92182	3	4.400	1	192	1	0	0	1	0	[125..150)
2387	28	2	51	94720	4	1.800	3	0	0	0	0	0	1	[50..75)
2500	28	2	121	92096	2	2.000	1	341	0	0	0	1	0	[100..125)
2685	28	2	101	90280	4	2.100	3	0	0	0	0	1	0	[100..125)
2807	27	2	129	90009	2	3.300	1	0	0	1	0	0	0	[125..150)
2860	27	2	20	95064	4	0.500	3	0	0	0	0	1	0	[0..25)
2884	28	2	48	93943	4	2.100	3	0	0	0	0	1	1	[25..50)
2897	28	2	34	92161	4	1.300	3	0	0	0	0	0	0	[25..50)
2951	26	2	132	94720	2	2.400	3	0	1	0	0	0	1	[125..150)
3037	27	2	158	95060	3	0.400	2	0	1	0	1	1	0	[150..175)
3040	28	2	33	95814	3	1.000	1	167	0	0	0	1	0	[25..50)
3055	28	2	111	94305	4	2.300	3	0	0	0	0	1	0	[100..125)
3121	28	2	13	91791	4	0.400	1	0	0	0	0	0	1	[0..25)
3228	27	2	45	94305	2	1.700	2	0	0	0	0	0	1	[25..50)
3373	28	2	182	92660	3	7.200	2	442	1	0	1	1	1	[175..200)
3469	26	2	79	95630	2	2.500	1	0	0	0	0	1	0	[75..100)
3494	29	2	31	91330	4	1.500	2	0	0	0	0	0	0	[25..50)
3579	28	2	84	94305	1	2.900	3	102	0	1	1	0	1	[75..100)
3663	26	2	60	94111	4	1.600	1	0	0	0	1	1	1	[50..75)
3751	26	2	12	94591	4	1.000	1	0	0	0	0	1	0	[0..25)
3809	26	2	62	94080	4	1.600	1	0	0	1	0	0	0	[50..75)
3875	26	2	119	95351	2	0.600	1	381	0	0	0	1	1	[100..125)
3885	32	2	69	93943	4	4.000	3	102	0	0	0	1	0	[50..75)
3932	26	2	55	94305	3	0.700	2	0	0	0	0	1	0	[50..75)
4048	27	2	48	90049	2	1.600	3	119	0	1	0	1	0	[25..50)
4085	28	2	53	94609	3	2.400	2	0	0	0	0	1	0	[50..75)
4100	27	2	41	90254	2	1.700	2	0	0	0	0	1	0	[25..50)
4113	28	2	41	93118	3	1.100	2	161	0	0	0	1	0	[25..50)
4185	26	2	82	91950	2	2.500	1	199	0	0	0	0	0	[75..100)
4265	27	2	44	93943	4	0.600	2	0	0	1	1	1	0	[25..50)
4337	26	2	182	93010	2	3.200	2	0	1	0	0	0	0	[175..200)
4362	28	2	55	93940	3	1.100	2	0	0	0	0	0	0	[50..75)
4365	26	2	85	95020	2	2.500	1	0	0	0	0	0	0	[75..100)
4413	29	2	31	91775	4	1.500	2	0	0	0	0	0	1	[25..50)
4508	27	2	85	94117	1	1.900	1	0	0	0	0	1	1	[75..100)
4563	28	2	188	92350	2	4.500	1	0	0	1	0	1	0	[175..200)
4757	26	2	135	94588	1	1.500	1	0	0	0	0	0	0	[125..150)
4769	26	2	20	95064	4	1.000	1	116	0	0	0	0	0	[0..25)
4772	26	2	95	92130	3	0.800	1	0	0	0	0	1	0	[75..100)

	Age	Experience	Income	ZIPCode	Family	CCAvg	Education	Mortgage	Personal_Loan	Securities_Account	CD_Account	Online	CreditCard	Income_bin
74	28	3	135	94611	2	3.300	1	0	0	0	0	0	1	[125..150)
177	29	3	65	94132	4	1.800	2	244	0	0	0	0	0	[50..75)
183	29	3	148	92173	3	4.100	1	0	1	0	0	1	0	[125..150)
198	27	3	59	94123	4	0.000	1	90	0	1	0	1	0	[50..75)
202	30	3	68	94306	4	2.000	2	0	0	0	0	1	0	[50..75)
216	27	3	125	95521	2	0.600	1	0	0	0	0	0	0	[100..125)
239	28	3	52	94112	4	1.700	2	0	0	0	0	0	0	[50..75)
272	29	3	45	95023	4	0.200	1	158	0	0	0	1	1	[25..50)
338	29	3	153	93657	2	2.000	1	392	0	0	0	0	0	[150..175)
399	28	3	84	90024	4	0.200	1	0	0	0	0	1	1	[75..100)
421	28	3	115	92333	4	3.100	2	0	1	0	0	0	0	[100..125)
425	28	3	28	90505	4	0.800	1	0	0	0	0	1	0	[25..50)
457	29	3	69	94303	3	0.300	3	0	0	0	0	0	0	[50..75)
552	28	3	52	90024	4	2.200	1	230	0	0	0	1	0	[50..75)
562	28	3	85	94035	1	0.800	2	0	0	0	0	1	1	[75..100)
581	28	3	55	94521	4	2.200	1	0	0	0	0	1	1	[50..75)
590	29	3	39	94612	4	2.100	3	0	0	0	0	1	0	[25..50)
604	28	3	70	90245	4	2.200	1	240	0	0	0	0	1	[50..75)
607	28	3	170	95014	1	0.100	3	0	1	0	0	0	0	[150..175)
731	28	3	90	90066	2	3.300	1	0	0	0	0	1	1	[75..100)
760	29	3	52	92122	3	1.100	2	0	0	0	0	1	0	[50..75)
789	29	3	31	92126	4	0.300	2	0	0	0	0	1	0	[25..50)
799	29	3	39	95051	4	2.100	3	0	0	0	0	1	0	[25..50)
840	27	3	94	92373	2	0.200	1	310	0	0	0	0	1	[75..100)
878	33	3	74	95616	4	4.000	3	0	0	0	0	0	0	[50..75)
899	30	3	172	91302	3	3.400	2	0	1	0	0	0	1	[150..175)
906	29	3	154	94720	2	2.000	1	130	0	0	0	0	0	[150..175)
931	27	3	43	91302	1	1.000	3	0	0	0	0	1	0	[25..50)
995	28	3	45	94305	2	1.600	3	0	0	0	0	1	1	[25..50)
1009	28	3	25	91330	2	0.900	3	140	0	0	0	1	0	[0..25)
1010	27	3	98	95616	2	2.500	1	361	0	1	1	1	1	[75..100)
1019	29	3	30	91745	4	0.300	2	157	0	0	0	0	0	[25..50)
1022	27	3	118	95605	1	3.300	2	0	1	0	0	1	0	[100..125)
1077	29	3	175	90095	3	3.300	3	329	1	0	0	1	0	[150..175)
1083	28	3	65	95014	3	2.600	3	0	0	1	0	0	0	[50..75)
1093	27	3	40	94550	3	0.100	2	111	0	0	0	1	0	[25..50)
1102	29	3	84	95023	1	2.900	3	0	0	0	0	1	0	[75..100)
1176	29	3	103	90049	4	3.400	1	0	1	0	0	1	0	[100..125)
1177	28	3	71	90405	1	3.300	2	149	1	1	1	1	0	[50..75)
1194	29	3	41	94305	4	1.300	3	0	0	0	0	1	0	[25..50)
1286	29	3	50	94010	3	1.100	2	0	0	0	0	0	1	[25..50)
1316	28	3	51	94086	2	1.600	3	123	0	0	0	0	0	[50..75)
1321	27	3	123	95138	1	5.400	1	0	0	0	0	0	0	[100..125)
1377	27	3	109	93023	2	2.500	1	0	0	0	0	1	0	[100..125)
1386	27	3	72	95616	4	0.000	1	0	0	0	0	1	0	[50..75)
1390	29	3	80	94305	4	1.800	2	0	0	0	0	1	1	[75..100)
1424	29	3	92	94539	2	1.300	1	287	0	0	0	1	0	[75..100)
1437	28	3	123	92007	1	0.800	1	146	0	0	0	0	0	[100..125)
1588	29	3	55	95616	3	1.100	2	0	0	0	0	1	0	[50..75)
1618	29	3	29	94720	3	1.000	1	0	0	0	0	1	1	[25..50)
1642	27	3	84	95814	3	1.500	1	0	0	0	0	1	1	[75..100)
1701	29	3	108	94304	4	1.800	2	0	0	0	0	0	0	[100..125)
1711	27	3	201	95819	1	6.330	1	158	0	0	0	1	0	[200..225]
1744	28	3	29	91105	4	0.800	1	135	0	0	0	1	0	[25..50)
1755	28	3	55	92647	4	1.700	2	0	0	0	0	1	1	[50..75)
1778	27	3	32	94710	3	1.000	2	0	0	0	0	0	0	[25..50)
1785	29	3	190	94080	2	4.500	1	0	0	0	0	1	0	[175..200)
1802	29	3	121	92806	2	1.300	1	0	0	0	0	0	0	[100..125)
1811	28	3	11	94534	4	0.500	3	0	0	0	0	0	0	[0..25)
1875	27	3	112	90066	3	2.500	1	389	0	1	0	1	0	[100..125)
1970	27	3	148	92780	1	1.500	1	397	0	0	0	1	1	[125..150)
1975	29	3	113	94132	2	0.200	1	0	0	0	0	1	1	[100..125)
2022	33	3	71	93561	4	1.800	3	236	0	0	0	0	0	[50..75)
2029	30	3	61	92152	4	2.000	2	0	0	0	0	1	0	[50..75)
2052	28	3	120	94080	1	0.800	1	170	0	0	0	0	0	[100..125)
2059	28	3	173	92121	2	6.700	1	222	0	0	0	1	0	[150..175)
2072	29	3	39	95831	4	0.200	1	137	0	0	0	1	1	[25..50)
2146	27	3	30	93108	1	1.000	3	80	0	0	0	0	0	[25..50)
2147	27	3	20	92007	4	1.000	1	0	0	0	0	0	0	[0..25)
2163	33	3	69	92161	4	1.800	3	0	0	0	0	1	0	[50..75)
2164	27	3	104	92007	2	2.500	1	184	0	1	0	1	0	[100..125)
2190	27	3	110	96150	2	0.200	1	294	0	1	0	0	1	[100..125)
2215	28	3	193	94501	3	4.000	2	0	1	0	0	1	0	[175..200)
2261	30	3	150	94305	4	5.000	2	0	1	0	0	1	0	[125..150)
2268	27	3	105	94304	1	3.000	2	0	1	1	0	0	0	[100..125)
2272	27	3	90	91365	3	0.800	1	0	0	0	0	1	0	[75..100)
2276	29	3	172	92093	4	4.400	1	0	1	0	0	0	0	[150..175)
2296	27	3	82	94305	2	0.200	1	0	0	0	0	0	1	[75..100)
2443	28	3	161	92646	4	1.700	3	422	1	0	1	1	1	[150..175)
2489	29	3	41	92626	4	0.200	1	0	0	0	0	1	0	[25..50)
2492	28	3	134	96091	2	3.100	1	0	0	0	0	1	0	[125..150)
2516	28	3	74	94720	3	2.600	3	0	0	0	0	0	0	[50..75)
2741	29	3	49	90266	1	1.500	1	0	0	0	0	0	0	[25..50)
2789	27	3	34	90065	1	0.200	3	0	0	0	0	1	1	[25..50)
2843	27	3	20	95616	4	1.000	1	134	0	0	0	1	1	[0..25)
2853	28	3	54	94550	4	0.600	2	0	0	0	0	1	0	[50..75)
2918	28	3	142	93727	1	0.800	1	0	0	0	0	1	0	[125..150)
2940	27	3	43	90245	3	0.100	2	163	0	0	0	1	0	[25..50)
2963	29	3	41	94588	1	1.900	3	0	0	0	0	1	1	[25..50)
3012	29	3	172	92373	2	4.500	1	415	0	0	0	1	0	[150..175)
3070	28	3	74	91330	2	1.800	2	221	0	1	0	0	0	[50..75)
3180	27	3	103	92121	2	0.600	1	84	0	0	0	0	0	[100..125)
3201	28	3	81	92121	4	0.200	1	0	0	0	0	0	0	[75..100)
3340	29	3	54	94104	4	1.800	3	0	0	0	0	0	0	[50..75)
3350	28	3	95	90245	2	1.800	2	0	0	0	0	0	0	[75..100)
3389	27	3	88	92182	3	0.800	1	238	0	0	0	0	0	[75..100)
3390	29	3	73	94720	3	0.300	3	0	0	0	0	0	0	[50..75)
3453	29	3	31	94709	4	0.300	2	0	0	0	0	1	0	[25..50)
3463	28	3	149	92121	1	0.800	1	0	0	0	0	1	0	[125..150)
3503	29	3	53	95814	4	2.100	3	0	0	0	0	1	0	[50..75)
3583	30	3	33	95112	4	1.500	2	85	0	0	0	0	0	[25..50)
3592	33	3	20	94704	1	0.670	3	0	0	0	0	0	0	[0..25)
3623	28	3	45	91105	4	1.700	2	95	0	0	0	0	0	[25..50)
3667	27	3	59	94590	4	1.600	1	0	0	0	0	1	0	[50..75)
3728	28	3	118	91902	3	2.400	2	161	1	0	0	0	0	[100..125)
3745	27	3	119	90640	1	5.400	1	118	0	0	0	1	0	[100..125)
3776	27	3	135	93108	3	2.700	3	449	1	0	0	0	1	[125..150)
3914	27	3	35	94080	1	1.800	2	0	0	0	0	0	0	[25..50)
3945	29	3	123	92821	3	5.600	3	428	1	0	0	1	0	[100..125)
3968	28	3	78	93108	4	0.200	1	0	0	0	0	1	1	[75..100)
4042	29	3	190	92612	2	4.500	1	246	0	0	0	1	1	[175..200)
4061	33	3	59	91040	2	1.750	3	0	0	0	0	1	0	[50..75)
4098	27	3	75	90032	4	0.000	1	0	0	0	0	0	0	[50..75)
4129	29	3	10	91320	4	0.400	1	87	0	0	0	1	1	[0..25)
4139	29	3	81	95827	1	2.900	3	0	0	0	0	0	0	[75..100)
4179	29	3	91	94122	1	3.400	3	0	1	0	0	0	0	[75..100)
4274	30	3	79	91380	4	2.000	2	0	0	0	0	1	0	[75..100)
4341	28	3	53	94305	2	1.600	3	0	0	0	0	0	0	[50..75)
4351	30	3	32	94132	1	2.000	2	0	0	0	0	1	1	[25..50)
4370	27	3	18	93524	1	0.400	3	0	0	0	0	0	0	[0..25)
4456	29	3	35	94040	2	0.300	1	88	0	0	1	1	1	[25..50)
4515	29	3	49	94305	4	2.100	3	0	0	0	0	0	0	[25..50)
4663	28	3	115	92407	1	1.900	1	200	0	0	0	1	0	[100..125)
4681	27	3	68	95503	4	0.000	1	0	0	0	0	0	1	[50..75)
4688	29	3	69	92093	4	1.800	2	0	0	0	0	1	1	[50..75)
4714	27	3	81	90291	3	1.500	1	307	0	1	1	1	1	[75..100)
4872	27	3	69	94305	3	0.700	2	0	0	0	0	1	1	[50..75)
4952	29	3	53	94005	4	1.800	3	0	0	0	0	1	0	[50..75)
4995	29	3	40	92697	1	1.900	3	0	0	0	0	1	0	[25..50)

	ZIPCode	NoAccounts	PLoans	PercentLoans	PercentAccounts
0	94720	169	19	0.040	0.034
1	94305	127	13	0.027	0.025
10	92093	51	9	0.019	0.010
16	94304	45	8	0.017	0.009
3	90095	71	8	0.017	0.014
13	90089	46	8	0.017	0.009
20	92182	32	7	0.015	0.006
35	94022	25	6	0.013	0.005
2	95616	116	6	0.013	0.023
18	95051	34	6	0.013	0.007
23	95054	31	5	0.010	0.006
24	95814	30	5	0.010	0.006
6	92037	54	5	0.010	0.011
12	90245	50	5	0.010	0.010
73	94928	15	5	0.010	0.003
43	91380	22	5	0.010	0.004
44	92612	22	4	0.008	0.004
36	95060	25	4	0.008	0.005
32	93407	26	4	0.008	0.005
30	95039	26	4	0.008	0.005

	Age	Experience	Income	Family	Education	Securities_Account	CreditCard	CCAvg_Inc%	ZIPCode_90089	ZIPCode_91107	ZIPCode_91330	ZIPCode_94112	ZIPCode_94720
0	25	1	49	4	1	1	0	39	0	1	0	0	0
1	45	19	34	3	1	1	0	52	1	0	0	0	0
2	39	15	11	1	1	0	0	109	0	0	0	0	1
3	35	9	100	1	2	0	0	32	0	0	0	1	0
4	35	8	45	4	2	0	1	26	0	0	1	0	0

	Model	Train_Recall	Test_Recall
0	Initial decision tree model	1.000	0.830
1	Decision tree with restricted maximum depth	0.810	0.750
2	Decision treee with hyperparameter tuning	0.890	0.810
3	Decision tree with post-pruning	0.950	0.840

	Age	Experience	Income	Family	Education	Securities_Account	CreditCard	CCAvg_Inc%	ZIPCode_90089	ZIPCode_91107	ZIPCode_91330	ZIPCode_94112	ZIPCode_94720
0	25	1	49	4	1	1	0	39	0	1	0	0	0
1	45	19	34	3	1	1	0	52	1	0	0	0	0
2	39	15	11	1	1	0	0	109	0	0	0	0	1
3	35	9	100	1	2	0	0	32	0	0	0	1	0
4	35	8	45	4	2	0	1	26	0	0	1	0	0

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Personal Loan Bank Campaign¶

Context¶

Objectives¶

Data Dictionary¶

Setup¶

Basic Setup and Imports¶

Function Definitions¶

Quick Dataset Overview¶

Data Preparation¶

Univariate Analysis¶

Null Values¶

Single Feature Distributions Plots¶

Analysis Feature: Age¶

Analysis Feature: Experience¶

Analysis Feature: Income¶

Analysis Feature: ZIPCode¶

Analysis Feature: Family¶

Analysis Feature: Education¶

Analysis Feature: Credit Card Use¶

Analysis Feature: Online, CD Account, Credit Card, Security Account¶

Bivariate Analysis¶

Age vs Personal Loan¶

Experience vs Age for Negative Experience¶

Experience vs Personal Loan¶

Income vs Personal Loan¶

ZIPCode vs Personal Loan¶

Family vs Personal Loan¶

Credit Card Average Expenses vs Personal Loan¶

Education vs Personal Loan¶

Mortgage vs Personal Loan¶

Securities Account vs Personal Loan¶

CD Account vs Personal Loan¶

Online vs Personal Loan¶

Credit Card vs Personal Loan¶

Second Data Preparation¶

Logistic Regression Modelling¶

Model Performance Improvement¶

Decision Tree Modelling¶

Decision Tree Performance¶

Confusion Matrix¶

Visualization of Decision Tree¶

Reducing Over Fitting¶

Confusion Matrix - decision tree with depth restricted to 3¶

Visualization of Decision Tree¶

Using GridSearch for Hyperparameter tuning of our tree model¶

Confusion Matrix - decision tree with tuned hyperparameters¶

Decision Tree Visualization¶

Cost Complexity Pruning¶

Accuracy vs alpha for training and testing sets¶

Confusion Matrix - post-pruned decision tree¶

Comparing Logistic Regression and Decision Tree¶

Conclusions¶

Recommendations¶

	Age	Experience	Income	Family	Education	Securities_Account	CreditCard	CCAvg_Inc%	ZIPCode_90089	ZIPCode_91107	ZIPCode_91330	ZIPCode_94112	ZIPCode_94720
0	25	1	49	4	1	1	0	39	0	1	0	0	0
1	45	19	34	3	1	1	0	52	1	0	0	0	0
2	39	15	11	1	1	0	0	109	0	0	0	0	1
3	35	9	100	1	2	0	0	32	0	0	0	1	0
4	35	8	45	4	2	0	1	26	0	0	1	0	0